├── 21.jpg ├── README.md ├── dict_file.txt ├── main.ipynb ├── test_file.txt ├── train_file.txt └── training.jpg /21.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sharpiless/paddleocr-enterprise-entity-recognition/ff0bf70d9d0d9d21f9449dcf880276d4ee654b74/21.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # “中国软件杯”大学生软件设计大赛二等奖开源代码——基于深度学习的企业实体识别 2 | 3 | 未经作者允许，本文禁止转载！ 4 | ## 代码地址 5 | 6 | ## 比赛简介： 7 | 为贯彻落实《国家中长期教育改革和发展规划纲要(2010-2020年)》，科学引导高校青年学子积极参加科研活动，切实增强自主创新能力和实际动手能力，实现应用型人才培养和产业需求的有效衔接，推动我国软件和信息技术服务业又好又快发展，工业和信息化部、教育部和江苏省人民政府共同主办了面向中国高校及广大海外院校在校学生(含高职)的纯公益性软件设计大赛。其中普通本科高校600余所，高职院校200余所，211、985高校百余所，累计近万名大学生参赛。 8 | 9 | ## 赛题介绍： 10 | > 着深度学习技术的发展，文字识别与自然语言处理近年来受到广泛关注。结合文字识别与自然语言处理技术解决传统方法无法处理的问题，成为企业提高自身竞争力的重要利器。 11 | 本赛题要求使用基于深度学习的文字识别与自然语言处理技术，识别商铺广告牌图片中的文字，从识别出的文字中提取出商铺名称。此系统涉及文字检测、文字识别、命名实体识别三种技术。 12 | 13 | ## 业务场景： 14 | > 企业实体识别主要应用在我们目前业务系统中的查证功能。查证功能的主要作用是将用户拍摄的店铺照片经过OCR识别后进行店铺名称的提取，然后通过店铺名称查询证照库，获取该店铺办理过的所有证照信息，方便用户进一步了解该店铺。在日常应用中，例如外出就餐时利用我们的系统随手拍摄要就餐的饭店门脸，系统会自动检测该饭店证照是否齐全以及所办证照的详细信息，方便用户做出就餐决策。 15 | 16 | ## 涉及工具： 17 | ### 1. PaddlePaddle原生框架： 18 | 百度出品的深度学习平台飞桨（PaddlePaddle）是主流深度学习框架中一款完全国产化的产品，与Google TensorFlow、Facebook Pytorch齐名。2016 年飞桨正式开源，是国内首个全面开源开放、技术领先、功能完备的产业级深度学习平台。相比国内其他平台，飞桨是一个功能完整的深度学习平台，也是唯一成熟稳定、具备大规模推广条件的深度学习平台。 19 | ### 2. PaddleOCR工具库： 20 | PaddleOCR是一个与OCR相关的开源项目，不仅支持超轻量级中文OCR预测模型，总模型仅8.6M（单模型支持中英文数字组合识别、竖排文本识别、长文本识别，其中检测模型DB（4.1M）+识别模型CRNN（4.5M）），而且提供多种文本检测训练算法（EAST、DB）和多种文本识别训练算法（Rosetta、CRNN、STAR-Net、RARE）。 21 | ### 3. Paddle-Lite轻量级推理框架： 22 | Paddle Lite是飞桨基于Paddle Mobile全新升级推出的端侧推理引擎，在多硬件、多平台以及硬件混合调度的支持上更加完备，为包括手机在内的端侧场景的AI应用提供高效轻量的推理能力，有效解决手机算力和内存限制等问题，致力于推动AI应用更广泛的落地 23 | 24 | ![](https://img-blog.csdnimg.cn/img_convert/0aca3395fbdf553d4858b6968ba41663.png) 25 | 26 | ## 设计思路： 27 | 28 | ![](https://img-blog.csdnimg.cn/img_convert/0a2ef9ae3e4beaa425dcabe79c0e4b61.png) 29 | 30 | ## 效果演示： 31 | ### 1. 手机APP： 32 | 33 | ![](https://img-blog.csdnimg.cn/img_convert/6d114d9574c735dd1763dcff20c011f9.png) 34 | 35 | ### 2. PC端软件： 36 | 37 | ![](https://img-blog.csdnimg.cn/img_convert/d3ca54d7934a52dc49e6cf96140c7a3e.png) 38 | 39 | 40 | ## Step-1 调用PaddleOCR进行文本检测 41 | 42 | 43 | ```python 44 | !pip install shapely 45 | !pip install pyclipper 46 | ``` 47 | 48 | Looking in indexes: https://mirror.baidu.com/pypi/simple/ 49 | Collecting shapely 50 | [?25l Downloading https://mirror.baidu.com/pypi/packages/98/f8/db4d3426a1aba9d5dfcc83ed5a3e2935d2b1deb73d350642931791a61c37/Shapely-1.7.1-cp37-cp37m-manylinux1_x86_64.whl (1.0MB) 51 |  |████████████████████████████████| 1.0MB 13.1MB/s eta 0:00:01 52 | [?25hInstalling collected packages: shapely 53 | Successfully installed shapely-1.7.1 54 | Looking in indexes: https://mirror.baidu.com/pypi/simple/ 55 | Collecting pyclipper 56 | [?25l Downloading https://mirror.baidu.com/pypi/packages/1a/2f/ba30c6fe34ac082232a89f00801ea087c231d714eb200e8a1faa439c90b5/pyclipper-1.2.0-cp37-cp37m-manylinux1_x86_64.whl (126kB) 57 |  |████████████████████████████████| 133kB 12.5MB/s eta 0:00:01 58 | [?25hInstalling collected packages: pyclipper 59 | Successfully installed pyclipper-1.2.0 60 | 61 | 62 | 63 | ```python 64 | from PIL import Image, ImageDraw, ImageFont 65 | from numpy import random 66 | import paddlehub as hub 67 | import numpy as np 68 | import paddle.fluid as fluid 69 | 70 | class Detector(object): 71 | def __init__(self): 72 | # 加载移动端预训练模型 73 | # self.ocr = hub.Module(name='chinese_ocr_db_crnn_mobile') 74 | # 服务端可以加载大模型，效果更好 75 | self.ocr = hub.Module(name='chinese_ocr_db_crnn_server') 76 | 77 | def feedCap(self, np_images, vis=True): 78 | 79 | results = self.ocr.recognize_text( 80 | # 图片数据，ndarray.shape 为 [H, W, C]，BGR格式； 81 | images=[np_images], 82 | use_gpu=True, # 是否使用 GPU；若使用GPU，请先设置CUDA_VISIBLE_DEVICES环境变量 83 | visualization=False, # 是否将识别结果保存为图片文件； 84 | box_thresh=0.5, # 检测文本框置信度的阈值； 85 | text_thresh=0.5) 86 | 87 | img = Image.fromarray(np_images[:, :, [2, 1, 0]]) 88 | draw = ImageDraw.Draw(img) # 图片上打印 89 | txt = [] 90 | 91 | for result in results: 92 | data = result['data'] 93 | for infomation in data: 94 | if vis: 95 | for i in range(5): 96 | pos = [(v[0]+i, v[1]+i) 97 | for v in infomation['text_box_position']] 98 | draw.polygon(pos, outline=(0, 255, 0)) 99 | txt.append(infomation['text']) 100 | 101 | # img = np.array(img)[:, :, [2, 1, 0]] 102 | return img, txt 103 | ``` 104 | 105 | 创建实例并查看图像 106 | 107 | 108 | ```python 109 | import os 110 | import cv2 111 | import matplotlib.pyplot as plt 112 | %matplotlib inline 113 | os.environ["CUDA_VISIBLE_DEVICES"] = '0' 114 | 115 | img = cv2.imread('21.jpg') 116 | plt.imshow(img[:, :, [2, 1, 0]]) 117 | ``` 118 | 119 | 120 | 121 | 122 | 123 | 使用OCR模型进行文本检测和识别 124 | 125 | 126 | ```python 127 | ocr_model = Detector() 128 | 129 | result, txt = ocr_model.feedCap(img) 130 | plt.imshow(result) 131 | plt.show() 132 | print(txt) 133 | ``` 134 | 135 | [2020-11-04 10:05:29,624] [ INFO] - Installing chinese_ocr_db_crnn_server module 136 | [2020-11-04 10:05:29,627] [ INFO] - Module chinese_ocr_db_crnn_server already installed in /home/aistudio/.paddlehub/modules/chinese_ocr_db_crnn_server 137 | [2020-11-04 10:05:30,104] [ INFO] - Installing chinese_text_detection_db_server module-1.0.2 138 | [2020-11-04 10:05:30,107] [ INFO] - Module chinese_text_detection_db_server-1.0.2 already installed in /home/aistudio/.paddlehub/modules/chinese_text_detection_db_server 139 | 140 | 141 | 142 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/2020123015484076.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDkzNjg4OQ==,size_16,color_FFFFFF,t_70#pic_center) 143 | 144 | 145 | 146 | ['正新鸡排', '遇见', '见', '天派', '天派', '元', 'Bh!', '31G元', '有爱就是', '超大派', '正新', '12元', '正新鸡排', '正新鸡排', '门市价18元', '再饮科1'] 147 | 148 | 149 | ## Step-2 使用百度地图API爬取店铺名称（正样本） 150 | 151 | 这一步需要首先在百度地图开放平台注册并且申请API 152 | 153 | ![](https://img-blog.csdnimg.cn/img_convert/7082f7b061bb7cf888cd6277c61547ec.png) 154 | 155 | ### 使用Python接口爬取店铺名 156 | 157 | 158 | ```python 159 | import requests 160 | from tqdm import tqdm 161 | 162 | 163 | def baidu_map_search(): 164 | # 注册->新建应用 http://lbsyun.baidu.com/ 165 | apk_key = "你申请的应用AK" 166 | 167 | url = "http://api.map.baidu.com/place/v2/search" 168 | 169 | types = [ 170 | '酒店', '美食', '购物', '生活服务', '丽人', '休闲娱乐', 171 | '运动健身', '教育培训', '文化传媒', '医疗', '汽车服务' 172 | ] 173 | 174 | with open('region.txt', 'r', encoding='utf-8') as f: 175 | regions = f.read() 176 | regions = regions.split('、') 177 | 178 | region_index = 0 179 | type_index = 0 180 | page = 0 181 | f = open('pos_{}.txt'.format(type_index), 'w', encoding='utf-8') 182 | while True: 183 | params = { 184 | "query": types[type_index], 185 | "output": "json", 186 | "ak": apk_key, 187 | "region": regions[region_index], 188 | "page_size": 20, 189 | "page_num": page, 190 | "scope": 1, 191 | "radius": 10000 192 | } 193 | 194 | page += 1 195 | 196 | response = requests.get(url, params) 197 | result = response.json() 198 | status = result.get("status") 199 | message = result.get("message") 200 | 201 | if status != 0 and status != 2: 202 | raise Exception(message) 203 | data = result.get("results", {}) 204 | if len(data) == 0: 205 | region_index += 1 206 | page = 0 207 | if region_index == len(regions): 208 | region_index = 0 209 | type_index += 1 210 | f.close() 211 | f = open('pos_{}.txt'.format(type_index), 'w', encoding='utf-8') 212 | if type_index == len(types): 213 | f.close() 214 | return 215 | 216 | print('{} {} page:{} num:{}'.format( 217 | regions[region_index], types[type_index], page, len(data))) 218 | for row in data: 219 | item = { 220 | "name": row.get("name", "") 221 | } 222 | for k, v in item.items(): 223 | if '市' in v: 224 | continue 225 | f.write(v.split('(')[0]+'\n') 226 | 227 | 228 | if True: 229 | 230 | baidu_map_search() 231 | 232 | results = [] 233 | for i in range(11): 234 | with open('pos_{}.txt'.format(i), 'r', encoding='utf-8') as f: 235 | data = f.read().splitlines() 236 | for v in tqdm(data): 237 | if v[0] >= 'A' and v[0] <= 'z': 238 | continue 239 | if '州' in v or '县' in v or '市' in v or '区' in v or '盟' in v or '厕' in v: 240 | continue 241 | if v in results: 242 | continue 243 | results.append(v) 244 | 245 | with open('pos_ch.txt', 'w', encoding='utf-8') as f: 246 | for v in results: 247 | f.write(v+'\n') 248 | 249 | print(len(results)) 250 | ``` 251 | 252 | 负样本我们从百度&香港大学提出新的中文街景数据——C-SVT数据集中进行采集，收集部分店铺广告等接近店铺名称的文本作为负样本参与训练。 253 | 254 | ![](https://img-blog.csdnimg.cn/img_convert/5b185606c8b1374e5e810ff63219b24f.png) 255 | 256 | 这里我们将正负样本处理成词向量，保存在txt中。 257 | 258 | ![](https://img-blog.csdnimg.cn/img_convert/9e80081b9f787f6b23ef05d8e6a6f496.png) 259 | 260 | ## Step-3 训练实体识别（二分类）模型 261 | 262 | 这里我们构建一个BiLSTM模型。 263 | 264 | ![](https://img-blog.csdnimg.cn/img_convert/4a8a851f5e7e46650baeecb10552c4c8.png) 265 | 266 | 267 | ### 训练模型 268 | 269 | 270 | ```python 271 | import numpy as np 272 | import paddle 273 | import paddle.fluid as fluid 274 | from multiprocessing import cpu_count 275 | import matplotlib.pyplot as mp 276 | 277 | def mydata_mapper(sample): 278 | data,label=sample 279 | val=[int(w) for w in data.split(',')] 280 | return val,int(label) 281 | 282 | ##读取器 283 | def train_reader(train_data_file): 284 | def reader(): 285 | with open(train_data_file,'r',encoding='utf8') as f: 286 | lines=f.readlines() 287 | np.random.shuffle(lines) 288 | for line in lines: 289 | data,label=line.split('\t') 290 | yield data,label 291 | return fluid.io.xmap_readers(mydata_mapper,reader,cpu_count(),1024) 292 | 293 | def test_reader(test_data_file): 294 | def reader(): 295 | with open(test_data_file,'r',encoding='utf8') as f: 296 | lines=f.readlines() 297 | np.random.shuffle(lines) 298 | for line in lines: 299 | data,label=line.split('\t') 300 | yield data,label 301 | return fluid.io.xmap_readers(mydata_mapper,reader,cpu_count(),1024) 302 | 303 | #模型TextCNN 304 | def cnn_net(data,dict_dim,class_dim,emb_dim=128,hid_dim=128,hid_dim2=98): 305 | #embedding 层 306 | emb=paddle.fluid.layers.embedding(input=data,size=[dict_dim,emb_dim]) 307 | conv_1=fluid.nets.sequence_conv_pool(input=emb,filter_size=2,num_filters=hid_dim,act='tanh',pool_type='sqrt') 308 | conv_2=fluid.nets.sequence_conv_pool(input=emb,filter_size=3,num_filters=hid_dim2,act='tanh',pool_type='sqrt') 309 | output=fluid.layers.fc(input=[conv_1,conv_2],size=class_dim,act='softmax') 310 | return output 311 | 312 | # 定义长短期记忆网络 313 | def lstm_net(ipt, input_dim): 314 | # 嵌入层 315 | emb = fluid.layers.embedding(input=ipt, size=[input_dim, 32], is_sparse=True) # 以数据的IDs作为输入 316 | # 全连接层 317 | fc1 = fluid.layers.fc(input=emb, size=32) 318 | # 进行长短期记忆操作 319 | lstm1, _ = fluid.layers.dynamic_lstm(input=fc1, #返回：隐藏状态（hidden state），LSTM的神经元状态 320 | size=32) #size=4*hidden_size 321 | # 第一个最大序列池 322 | fc2 = fluid.layers.sequence_pool(input=fc1, pool_type='max') 323 | # 第二个最大序列池 324 | lstm2 = fluid.layers.sequence_pool(input=lstm1, pool_type='max') 325 | # 输出层 326 | out = fluid.layers.fc(input=[fc2, lstm2], size=2, act='softmax') # 以softmax作为全连接的输出层，大小为2,也就是正负面 327 | 328 | return out 329 | ``` 330 | 331 | 2020-11-04 16:28:28,615-INFO: font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts'] 332 | 2020-11-04 16:28:29,038-INFO: generated new fontManager 333 | 334 | 335 | 336 | ```python 337 | import os 338 | 339 | def get_dict_len(): 340 | with open('dict_file.txt','r',encoding='utf-8') as f: 341 | lines=f.readlines() 342 | return len(eval(lines[0])) 343 | 344 | poch = 20 345 | model_save_path='work/new_classify_word/' 346 | words=fluid.layers.data(name='x',shape=[1],dtype='int64',lod_level=1) 347 | label=fluid.layers.data(name='label',shape=[1],dtype='int64') 348 | lengt=get_dict_len() 349 | print(lengt) 350 | 351 | # 获取文本CNN 352 | # model=cnn_net(words,lengt,2) 353 | 354 | # # 获取长短期记忆网络 355 | model = lstm_net(words, lengt) 356 | 357 | cost=fluid.layers.cross_entropy(input=model,label=label) 358 | 359 | avg_cost=fluid.layers.mean(cost) 360 | acc=fluid.layers.accuracy(input=model,label=label) 361 | 362 | optomizer=fluid.optimizer.AdagradOptimizer(learning_rate=0.005) 363 | opt=optomizer.minimize(avg_cost) 364 | test_program=fluid.default_main_program().clone(for_test=True) 365 | use_cuda=False 366 | place=fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 367 | exe=fluid.Executor(place) 368 | exe.run(fluid.default_startup_program()) 369 | my_train_reader=paddle.batch(train_reader('train_file.txt'),batch_size=128) 370 | my_test_reader=paddle.batch(test_reader('test_file.txt'),batch_size=128) 371 | 372 | my_feeder=fluid.DataFeeder(place=place,feed_list=[words,label]) 373 | accs=[] 374 | costs=[] 375 | batchs=[] 376 | times=0 377 | for pass_id in range(poch+1): 378 | for batch_id,data in enumerate(my_train_reader()): 379 | #print(data) 380 | times+=1 381 | train_cost,train_acc=exe.run(program=fluid.default_main_program(),feed=my_feeder.feed(data),fetch_list=[avg_cost,acc]) 382 | if batch_id%100==0: 383 | print('pass_id:%d,batch_id%d,train_cost%f,train_acc%f'%(pass_id,batch_id,train_cost[0],train_acc[0])) 384 | accs.append(train_acc[0]) 385 | costs.append(train_cost[0]) 386 | batchs.append(times) 387 | test_costs=[] 388 | test_accs=[] 389 | for batch_id,data in enumerate(my_test_reader()): 390 | test_cost,test_acc=exe.run(program=test_program, 391 | feed=my_feeder.feed(data), 392 | fetch_list=[avg_cost,acc]) 393 | test_costs.append(test_cost[0]) 394 | test_accs.append(test_acc[0]) 395 | avg_test_cost=sum(test_costs)/len(test_costs) 396 | avg_test_acc=sum(test_accs)/len(test_accs) 397 | 398 | if not os.path.exists(model_save_path): 399 | os.mkdir(model_save_path) 400 | 401 | if pass_id % 4 == 0: 402 | print('pass_id:%d,avg_test_cost%f,avg_test_acc%f'%(pass_id,avg_test_cost,avg_test_acc)) 403 | fluid.io.save_inference_model(model_save_path, 404 | feeded_var_names=[words.name], 405 | target_vars=[model], 406 | executor=exe) 407 | print('保存模型完成！') 408 | mp.title('训练图') 409 | mp.xlabel('iters',fontsize=20) 410 | mp.ylabel('cost/acc',fontsize=14) 411 | mp.plot(batchs,costs,color='red',label='training costs') 412 | mp.plot(batchs,accs,color='blue',label='training accs') 413 | mp.legend() 414 | mp.grid() 415 | mp.savefig('training.jpg') 416 | mp.show() 417 | ``` 418 | 419 | 420 | pass_id:17,batch_id0,train_cost0.143791,train_acc0.953125 421 | pass_id:17,batch_id100,train_cost0.092052,train_acc0.976562 422 | pass_id:18,batch_id0,train_cost0.116342,train_acc0.968750 423 | pass_id:18,batch_id100,train_cost0.138811,train_acc0.929688 424 | pass_id:19,batch_id0,train_cost0.133741,train_acc0.937500 425 | pass_id:19,batch_id100,train_cost0.148492,train_acc0.921875 426 | pass_id:20,batch_id0,train_cost0.113202,train_acc0.968750 427 | pass_id:20,batch_id100,train_cost0.114101,train_acc0.945312 428 | pass_id:20,avg_test_cost0.312817,avg_test_acc0.870767 429 | 保存模型完成！ 430 | 431 | ![在这里插入图片描述](https://img-blog.csdnimg.cn/2020123015481828.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDkzNjg4OQ==,size_16,color_FFFFFF,t_70#pic_center) 432 | 433 | 434 | ```python 435 | fluid.io.save_inference_model(dirname="infer.model", feeded_var_names=["x"], target_vars=[model], executor=exe) 436 | print("保存成功!") 437 | ``` 438 | 439 | ### 测试模型 440 | 441 | 442 | ```python 443 | import paddle.fluid as fluid 444 | import numpy as np 445 | 446 | def get_data(s): 447 | with open('dict_file.txt','r',encoding='utf-8')as f: 448 | dict_txt=eval(f.readlines()[0]) 449 | ret=[] 450 | for w in s: 451 | if not w in dict_txt.keys(): 452 | w='' 453 | ret.append((int(dict_txt[w]))) 454 | return ret 455 | 456 | 457 | use_cuda=False 458 | model_save_path='work/new_classify_word/' 459 | place=fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 460 | exe=fluid.Executor(place) 461 | exe.run(fluid.default_startup_program()) 462 | infer_program,infer_feeder,infer_target=fluid.io.load_inference_model(dirname=model_save_path,executor=exe) 463 | data=get_data('佳信房产') 464 | data1=get_data('联系电话110') 465 | text=[] 466 | text.append(data) 467 | text.append(data1) 468 | length=[[len(c) for c in text]] 469 | #print(length) 470 | tesor_word=fluid.create_lod_tensor(text,length,place) 471 | result=exe.run(infer_program,feed={'x':tesor_word},fetch_list=infer_target) 472 | names=['非主体', '主体'] 473 | for i in range(len(text)): 474 | infer_id=np.argsort(result)[0][i][-1] 475 | ext.append(data) 476 | text.append(data1) 477 | length=[[len(c) for c in text]] 478 | #print(length) 479 | tesor_word=fluid.create_lod_tensor(text,length,place) 480 | result=exe.run(infer_program,feed={'x':tesor_word},fetch_list=infer_target) 481 | names=['非主体', '主体'] 482 | for i in range(len(text)): 483 | infer_id=np.argsort(result)[0][i][-1] 484 | print('预测结果是：%s,概率为%f' %(names[infer_id],result[0][i][infer_id])) 485 | ``` 486 | 487 | 预测结果是：主体,概率为0.976609 488 | 预测结果是：非主体,概率为0.979655 489 | 490 | -------------------------------------------------------------------------------- /dict_file.txt: -------------------------------------------------------------------------------- 1 | {'7': 0, '天': 1, '酒': 2, '店': 3, '全': 4, '季': 5, '优': 6, '品': 7, '汉': 8, '庭': 9, '如': 10, '家': 11, '锦': 12, '江': 13, '之': 14, '星': 15, '燕': 16, '清': 17, '园': 18, '宾': 19, '馆': 20, '海': 21, '友': 22, '温': 23, '馨': 24, '北': 25, '京': 26, '安': 27, '瑞': 28, '嘉': 29, '连': 30, '锁': 31, '速': 32, '8': 33, '·': 34, 'n': 35, 'e': 36, 'o': 37, '喆': 38, '啡': 39, '9': 40, '选': 41, '布': 42, '丁': 43, '凯': 44, '健': 45, '身': 46, '禧': 47, '龙': 48, '飘': 49, 'H': 50, 'O': 51, 'M': 52, 'E': 53, '新': 54, '华': 55, '宜': 56, '君': 57, '山': 58, '水': 59, '时': 60, '尚': 61, '金': 62, '辉': 63, '国': 64, '际': 65, '商': 66, '务': 67, '会': 68, '议': 69, '大': 70, '地': 71, '来': 72, '欣': 73, '都': 74, '格': 75, '林': 76, '豪': 77, '泰': 78, '百': 79, '快': 80, '捷': 81, '德': 82, '世': 83, '纪': 84, '千': 85, '子': 86, '桐': 87, '程': 88, '首': 89, '科': 90, '四': 91, '兰': 92, '亭': 93, '云': 94, '冈': 95, '柚': 96, '米': 97, '寓': 98, '鸿': 99, '伟': 100, '成': 101, '富': 102, '侨': 103, '曲': 104, '观': 105, '唐': 106, '假': 107, '日': 108, '睿': 109, '士': 110, '主': 111, '题': 112, '鑫': 113, '珠': 114, '佳': 115, '贯': 116, '通': 117, '现': 118, '代': 119, '居': 120, '西': 121, '里': 122, '伯': 123, '豌': 124, '豆': 125, '尖': 126, '儿': 127, '青': 128, '年': 129, '旅': 130, '舍': 131, '汇': 132, '阁': 133, '钻': 134, '石': 135, '蓝': 136, '宝': 137, '苑': 138, '临': 139, '南': 140, '宫': 141, '民': 142, '族': 143, '泉': 144, '养': 145, '生': 146, '润': 147, '鸣': 148, '镝': 149, '奇': 150, '波': 151, '菲': 152, '特': 153, '七': 154, '月': 155, '真': 156, '爱': 157, '精': 158, '途': 159, '1': 160, '迷': 161, '你': 162, '派': 163, '柏': 164, '维': 165, '也': 166, '纳': 167, '必': 168, '思': 169, '桔': 170, '晶': 171, '津': 172, '逸': 173, '明': 174, '亚': 175, '丰': 176, '绅': 177, '莲': 178, '花': 179, '城': 180, '东': 181, '方': 182, '便': 183, '光': 184, '栈': 185, '中': 186, '汽': 187, '信': 188, '半': 189, '岛': 190, '贝': 191, '壳': 192, '川': 193, '美': 194, '钰': 195, '巨': 196, '合': 197, '悦': 198, '丽': 199, '枫': 200, '达': 201, '春': 202, '门': 203, '永': 204, '乐': 205, '经': 206, '奥': 207, '利': 208, '太': 209, '阳': 210, '雨': 211, '界': 212, '盛': 213, '澜': 214, '湾': 215, '智': 216, '内': 217, '蒙': 218, '古': 219, '马': 220, '驿': 221, '3': 222, '6': 223, '5': 224, '诚': 225, '实': 226, '良': 227, '易': 228, '佰': 229, '公': 230, '度': 231, '老': 232, '头': 233, '莫': 234, '瀚': 235, '九': 236, '号': 237, '荔': 238, '邦': 239, '翔': 240, '轩': 241, '骏': 242, '怡': 243, '比': 244, '坲': 245, '梦': 246, '想': 247, 'L': 248, 'F': 249, 'T': 250, '情': 251, '名': 252, '筑': 253, '鹅': 254, '湖': 255, '村': 256, '滨': 257, '印': 258, '象': 259, '式': 260, '环': 261, '梧': 262, '小': 263, '蚂': 264, '蚁': 265, '集': 266, '贤': 267, '服': 268, '斯': 269, '上': 270, '空': 271, '虹': 272, '桥': 273, '廷': 274, '白': 275, '玉': 276, '车': 277, '立': 278, '客': 279, '宿': 280, '适': 281, '轻': 282, '奢': 283, '法': 284, '莱': 285, '麦': 286, '福': 287, '示': 288, '单': 289, '和': 290, '颐': 291, '至': 292, '澳': 293, '紫': 294, '藤': 295, '开': 296, '元': 297, '曼': 298, '外': 299, '好': 300, '邮': 301, '电': 302, '厦': 303, '浦': 304, '沐': 305, '心': 306, '堂': 307, '词': 308, '强': 309, '宇': 310, '雅': 311, '约': 312, '臻': 313, '路': 314, '豹': 315, '雁': 316, '荡': 317, '碧': 318, '坊': 319, '泽': 320, '聚': 321, '莘': 322, '加': 323, '芝': 324, '风': 325, '0': 326, '裕': 327, '泊': 328, '旺': 329, '又': 330, '见': 331, '喜': 332, '善': 333, '黄': 334, '河': 335, '缘': 336, '致': 337, '幸': 338, '及': 339, '第': 340, '久': 341, '升': 342, '语': 343, '休': 344, '闲': 345, '质': 346, '巢': 347, '拉': 348, '熊': 349, '猫': 350, '罗': 351, '重': 352, '庆': 353, '何': 354, '夜': 355, '伊': 356, '可': 357, '莎': 358, '盈': 359, '田': 360, '劲': 361, '力': 362, '创': 363, '阿': 364, '尔': 365, '非': 366, '繁': 367, '凡': 368, '斐': 369, '皇': 370, '极': 371, '仪': 372, '杨': 373, '银': 374, '普': 375, '垫': 376, '贵': 377, '秘': 378, '境': 379, '渝': 380, '与': 381, '万': 382, '卫': 383, '营': 384, '博': 385, '钢': 386, '铭': 387, '学': 388, '阅': 389, '定': 390, '一': 391, '艺': 392, '术': 393, '托': 394, '高': 395, '隆': 396, '丝': 397, '画': 398, '遇': 399, '尼': 400, '涪': 401, '陵': 402, '饭': 403, '乡': 404, '赛': 405, '住': 406, '灰': 407, '胶': 408, '囊': 409, '欧': 410, '登': 411, '萃': 412, '拾': 413, '柒': 414, '武': 415, '自': 416, '然': 417, '卓': 418, '鼎': 419, 'V': 420, '岸': 421, '堡': 422, '香': 423, '薰': 424, '.': 425, '帕': 426, '克': 427, '顿': 428, '微': 429, '漫': 430, '夕': 431, '栖': 432, '设': 433, '计': 434, '景': 435, '寒': 436, '漾': 437, '洛': 438, '鸟': 439, '筱': 440, '游': 441, '吉': 442, '屋': 443, '听': 444, '诗': 445, '行': 446, '政': 447, '舒': 448, '贸': 449, '五': 450, '洲': 451, '气': 452, '希': 453, '欢': 454, '朋': 455, '祥': 456, '茂': 457, '翡': 458, '翠': 459, '而': 460, '恒': 461, '凇': 462, '铂': 463, '曹': 464, '童': 465, '话': 466, '巴': 467, '厘': 468, '向': 469, '葵': 470, '素': 471, '颜': 472, '2': 473, '红': 474, '囍': 475, '后': 476, '街': 477, '伦': 478, '威': 479, '同': 480, '荆': 481, '谢': 482, '湘': 483, '壶': 484, '以': 485, '乌': 486, '文': 487, '化': 488, '双': 489, '磐': 490, '岚': 491, '4': 492, '绣': 493, '趣': 494, '雾': 495, '港': 496, '圣': 497, '松': 498, '王': 499, '雪': 500, '帝': 501, '蛟': 502, '夏': 503, '添': 504, '摄': 505, '影': 506, '基': 507, '投': 508, '瓜': 509, '满': 510, '俗': 511, '下': 512, '韩': 513, '屯': 514, '赵': 515, '社': 516, '站': 517, '暨': 518, '珀': 519, '沙': 520, '咀': 521, '仔': 522, '平': 523, '洋': 524, '粤': 525, '铜': 526, '锣': 527, '荟': 528, '诺': 529, '角': 530, '哥': 531, '孛': 532, '围': 533, '荃': 534, '康': 535, '得': 536, '英': 537, '晴': 538, '炮': 539, '台': 540, '朗': 541, '最': 542, '绰': 543, '仕': 544, '弥': 545, '敦': 546, '愉': 547, '彩': 548, '卡': 549, '塘': 550, '冠': 551, '晋': 552, '远': 553, '机': 554, '场': 555, '隅': 556, '潘': 557, '多': 558, '唯': 559, '御': 560, '叶': 561, '娜': 562, '茶': 563, '-': 564, '磡': 565, '泛': 566, '艾': 567, '六': 568, '旭': 569, '网': 570, '荷': 571, '楼': 572, '汀': 573, '迪': 574, '秀': 575, '问': 576, '芬': 577, '畔': 578, 'U': 579, '李': 580, '活': 581, '道': 582, '零': 583, '八': 584, '蒲': 585, '简': 586, '二': 587, '十': 588, '甫': 589, 'K': 590, '挪': 591, '舟': 592, '灏': 593, '俪': 594, '救': 595, '军': 596, '卜': 597, '廉': 598, '銮': 599, '遨': 600, '熹': 601, '长': 602, '壹': 603, '桂': 604, '麻': 605, '雀': 606, '翩': 607, '板': 608, '工': 609, '业': 610, '赋': 611, '房': 612, 'J': 613, 'N': 614, '哲': 615, '边': 616, '渡': 617, '坑': 618, '庄': 619, '峰': 620, '协': 621, '理': 622, '火': 623, '礼': 624, '物': 625, '浪': 626, '涛': 627, 'W': 628, '技': 629, '悠': 630, '禾': 631, '种': 632, '植': 633, '@': 634, '油': 635, '督': 636, '教': 637, '探': 638, '索': 639, '岭': 640, 'A': 641, '座': 642, '人': 643, '葡': 644, '励': 645, '狮': 646, '梅': 647, '濠': 648, '璟': 649, '事': 650, '发': 651, '摩': 652, '望': 653, '期': 654, '置': 655, '韦': 656, '镇': 657, '兴': 658, '三': 659, '有': 660, '限': 661, '司': 662, '锋': 663, '旋': 664, '财': 665, '神': 666, '迎': 667, '鹭': 668, '别': 669, '墅': 670, '竹': 671, '回': 672, '榕': 673, '寰': 674, '济': 675, '黑': 676, '擎': 677, '潭': 678, '群': 679, '亿': 680, '排': 681, '联': 682, '薪': 683, '广': 684, '茵': 685, '妇': 686, '步': 687, '廊': 688, '仓': 689, '昌': 690, '者': 691, '蕙': 692, '恋': 693, '牙': 694, '域': 695, '总': 696, '统': 697, ',': 698, '荣': 699, '益': 700, '筷': 701, '意': 702, '关': 703, '口': 704, '圆': 705, '蕃': 706, '茄': 707, '源': 708, '胜': 709, '涮': 710, '羊': 711, '肉': 712, '顺': 713, '烤': 714, '鸭': 715, '菜': 716, '魁': 717, '吊': 718, '梨': 719, '汤': 720, '婆': 721, '辣': 722, '贡': 723, '院': 724, '蜀': 725, '碗': 726, '常': 727, '底': 728, '捞': 729, '锅': 730, '流': 731, '妈': 732, '兔': 733, '飞': 734, '的': 735, '鸡': 736, 'l': 737, 'y': 738, 'i': 739, 'g': 740, ' ': 741, 'c': 742, 'h': 743, 'k': 744, '咸': 745, '亨': 746, '鸦': 747, '记': 748, '味': 749, '俏': 750, '样': 751, '潇': 752, '府': 753, '姓': 754, '彭': 755, '铁': 756, '肥': 757, '肠': 758, '儒': 759, '宴': 760, '点': 761, '串': 762, '吧': 763, '很': 764, '前': 765, '赣': 766, '根': 767, '坎': 768, '湊': 769, '憩': 770, '厨': 771, '娘': 772, '莆': 773, '呷': 774, '哺': 775, '谭': 776, '血': 777, '肴': 778, '井': 779, '灶': 780, '潮': 781, '粥': 782, '攀': 783, '郡': 784, '肝': 785, '炉': 786, '冯': 787, '蝎': 788, '牡': 789, '丹': 790, '鲍': 791, '鱼': 792, '食': 793, '啤': 794, '器': 795, '餐': 796, '厅': 797, '玫': 798, '瑰': 799, '助': 800, '框': 801, '胡': 802, '卤': 803, '煮': 804, '阎': 805, '炖': 806, '带': 807, '皮': 808, '碳': 809, '腿': 810, '是': 811, '乎': 812, '椒': 813, '酸': 814, '盗': 815, '当': 816, '劳': 817, '鼓': 818, '馒': 819, '垚': 820, '焱': 821, '馕': 822, '烧': 823, '吃': 824, '秦': 825, '陕': 826, '早': 827, '面': 828, '塑': 829, '牛': 830, '粉': 831, '疆': 832, '刀': 833, '削': 834, '间': 835, '脑': 836, '馋': 837, '传': 838, '冒': 839, '拿': 840, '本': 841, '杰': 842, '交': 843, '张': 844, '亮': 845, '烫': 846, '爷': 847, '腩': 848, '妮': 849, '正': 850, '醉': 851, '晓': 852, '寿': 853, '蘑': 854, '范': 855, '菇': 856, '桃': 857, '音': 858, '咖': 859, '榀': 860, '枣': 861, '糕': 862, '勒': 863, '玲': 864, '渔': 865, '刘': 866, '辈': 867, '鹏': 868, '砂': 869, '赤': 870, '那': 871, '鲜': 872, '体': 873, '验': 874, '奶': 875, '个': 876, '夫': 877, '蒸': 878, '树': 879, '姿': 880, '造': 881, '歌': 882, '亩': 883, '农': 884, '巷': 885, '这': 886, '些': 887, '忘': 888, '不': 889, '掉': 890, '独': 891, '孔': 892, '彤': 893, '莉': 894, '汕': 895, '慈': 896, '巫': 897, '槐': 898, '虾': 899, '离': 900, '愁': 901, '深': 902, '肯': 903, '靓': 904, '眼': 905, '贴': 906, '饺': 907, '谦': 908, '脆': 909, '魏': 910, '凉': 911, '斗': 912, '栗': 913, '炸': 914, '猪': 915, '姆': 916, '俄': 917, '权': 918, '坡': 919, '琦': 920, '珍': 921, '冰': 922, '恩': 923, '萨': 924, '搭': 925, '混': 926, '巧': 927, '妹': 928, '螺': 929, '蛳': 930, '煎': 931, '姜': 932, '虎': 933, '炭': 934, '饼': 935, '披': 936, '宅': 937, '急': 938, '送': 939, '噢': 940, '啦': 941, '惠': 942, '土': 943, '船': 944, '斋': 945, 'C': 946, '弗': 947, '书': 948, '矢': 949, '量': 950, '贰': 951, '杏': 952, '森': 953, '谷': 954, '粹': 955, '态': 956, '饮': 957, '私': 958, '就': 959, '膳': 960, '莜': 961, '苏': 962, '浙': 963, '忆': 964, '虞': 965, '肆': 966, '撸': 967, '墙': 968, '泓': 969, '霸': 970, '婚': 971, '滩': 972, '只': 973, '東': 974, '發': 975, '朱': 976, '打': 977, '姬': 978, '赞': 979, '腾': 980, '左': 981, '右': 982, '佛': 983, '蟹': 984, '朵': 985, '磨': 986, '倍': 987, '料': 988, '焰': 989, '先': 990, '闽': 991, '珂': 992, '庐': 993, '郎': 994, 'b': 995, 'u': 996, 's': 997, '鸢': 998, '尾': 999, '狠': 1000, '彼': 1001, '撒': 1002, '崎': 1003, '鮨': 1004, '玖': 1005, '酱': 1006, '绝': 1007, '脖': 1008, '茳': 1009, '看': 1010, '到': 1011, '徐': 1012, '解': 1013, '疯': 1014, '焙': 1015, '伴': 1016, '蛋': 1017, '端': 1018, '制': 1019, '芭': 1020, '弓': 1021, '虽': 1022, '焖': 1023, '举': 1024, '溶': 1025, '洞': 1026, '秧': 1027, '货': 1028, '纯': 1029, '说': 1030, '瑜': 1031, '痴': 1032, '储': 1033, '鳝': 1034, '侠': 1035, '席': 1036, '姐': 1037, '翰': 1038, '纸': 1039, '煌': 1040, '蓉': 1041, '麒': 1042, '齿': 1043, '守': 1044, '柴': 1045, '亦': 1046, '寨': 1047, '坞': 1048, '仙': 1049, '官': 1050, '蛙': 1051, '嘿': 1052, '嘟': 1053, '菌': 1054, '峡': 1055, '匠': 1056, '直': 1057, '棚': 1058, '尊': 1059, '鲁': 1060, '牌': 1061, '甲': 1062, '木': 1063, '抄': 1064, '手': 1065, '怀': 1066, '廖': 1067, '棒': 1068, '甜': 1069, '奈': 1070, 'の': 1071, '慕': 1072, '&': 1073, '研': 1074, '琴': 1075, '樱': 1076, '朝': 1077, '延': 1078, '众': 1079, '稼': 1080, '萍': 1081, '草': 1082, '原': 1083, '胖': 1084, '男': 1085, '孩': 1086, '两': 1087, '故': 1088, '卑': 1089, '袁': 1090, '墨': 1091, '宗': 1092, '霍': 1093, '于': 1094, '章': 1095, '叁': 1096, '酿': 1097, '干': 1098, '怪': 1099, '丸': 1100, '慢': 1101, '詹': 1102, '骨': 1103, '玛': 1104, '爆': 1105, '肚': 1106, '镫': 1107, '初': 1108, '兆': 1109, '包': 1110, '烘': 1111, '咪': 1112, '聪': 1113, '嫂': 1114, '级': 1115, '椰': 1116, '汁': 1117, '煲': 1118, '姨': 1119, '糖': 1120, '扒': 1121, '室': 1122, '敍': 1123, '容': 1124, '运': 1125, '烟': 1126, '窗': 1127, '声': 1128, '翅': 1129, '争': 1130, '越': 1131, '贺': 1132, '雄': 1133, '鮮': 1134, '鍋': 1135, '佐': 1136, '邓': 1137, '寮': 1138, '少': 1139, '淮': 1140, '扬': 1141, '专': 1142, '菱': 1143, '糯': 1144, '糍': 1145, '忠': 1146, '线': 1147, '奀': 1148, '吞': 1149, '笼': 1150, '%': 1151, 'r': 1152, 'a': 1153, 'S': 1154, 't': 1155, '哈': 1156, '烈': 1157, '挞': 1158, '义': 1159, '陶': 1160, '档': 1161, '什': 1162, '沨': 1163, '凤': 1164, '凼': 1165, '黎': 1166, '出': 1167, '没': 1168, '腐': 1169, '柠': 1170, '檬': 1171, '露': 1172, '缅': 1173, '甸': 1174, '棉': 1175, '洪': 1176, '哩': 1177, '字': 1178, '芳': 1179, '师': 1180, '傅': 1181, '脚': 1182, '兵': 1183, '喱': 1184, '作': 1185, '笑': 1186, '收': 1187, '焕': 1188, '氹': 1189, '余': 1190, '毛': 1191, '琳': 1192, '琅': 1193, '旗': 1194, '舰': 1195, '综': 1196, '笃': 1197, '渌': 1198, '杯': 1199, '球': 1200, '乃': 1201, '陈': 1202, '诃': 1203, '帆': 1204, '图': 1205, '屉': 1206, '佬': 1207, '驰': 1208, '钧': 1209, '玥': 1210, '饱': 1211, '秋': 1212, '冬': 1213, '萄': 1214, '腊': 1215, '码': 1216, '勤': 1217, '晖': 1218, '企': 1219, '坂': 1220, '艮': 1221, '进': 1222, '补': 1223, '班': 1224, '弄': 1225, '塔': 1226, '保': 1227, '户': 1228, '陆': 1229, '俱': 1230, '部': 1231, '枝': 1232, '分': 1233, '暹': 1234, '逻': 1235, '殿': 1236, '吐': 1237, '许': 1238, '留': 1239, '铺': 1240, '溪': 1241, '节': 1242, '沪': 1243, '轮': 1244, '我': 1245, '薄': 1246, '顶': 1247, '”': 1248, '动': 1249, '息': 1250, 'I': 1251, '蜜': 1252, '密': 1253, '鹿': 1254, '典': 1255, '梓': 1256, '坤': 1257, '沛': 1258, '瀛': 1259, '任': 1260, '购': 1261, '楹': 1262, '果': 1263, '浓': 1264, '建': 1265, '昆': 1266, '灯': 1267, '萌': 1268, '宠': 1269, '氏': 1270, '孕': 1271, '婴': 1272, '具': 1273, '为': 1274, '授': 1275, '察': 1276, '产': 1277, '卖': 1278, '沟': 1279, '钱': 1280, '申': 1281, '耐': 1282, '蔬': 1283, '销': 1284, '展': 1285, '因': 1286, '装': 1287, '色': 1288, '毗': 1289, '邻': 1290, '办': 1291, '用': 1292, '习': 1293, '处': 1294, '鞋': 1295, '厂': 1296, '材': 1297, '硕': 1298, '祠': 1299, '调': 1300, '批': 1301, '售': 1302, '峪': 1303, '共': 1304, '无': 1305, '害': 1306, '药': 1307, '配': 1308, '订': 1309, '刚': 1310, '饰': 1311, '铝': 1312, '妆': 1313, '库': 1314, '贱': 1315, '薇': 1316, '睛': 1317, '镜': 1318, '谈': 1319, '宁': 1320, '淼': 1321, '冻': 1322, '屏': 1323, '蓬': 1324, '冷': 1325, '粮': 1326, '宏': 1327, '标': 1328, '准': 1329, '件': 1330, '玻': 1331, '璃': 1332, '霞': 1333, '剂': 1334, '瓷': 1335, '缔': 1336, '条': 1337, '浴': 1338, '茗': 1339, '赏': 1340, '奉': 1341, '慧': 1342, '乾': 1343, '能': 1344, '热': 1345, '泵': 1346, '孙': 1347, '艳': 1348, '振': 1349, '浑': 1350, '袋': 1351, '副': 1352, '领': 1353, '殊': 1354, '钟': 1355, '表': 1356, '数': 1357, '帘': 1358, '斌': 1359, '池': 1360, '璐': 1361, '蓟': 1362, '涟': 1363, '修': 1364, '佑': 1365, '裁': 1366, '寺': 1367, '启': 1368, '供': 1369, '蝶': 1370, '志': 1371, '卉': 1372, '份': 1373, '烛': 1374, '炳': 1375, '将': 1376, '掌': 1377, '蕾': 1378, '杂': 1379, '琉': 1380, '瓦': 1381, '盆': 1382, '洁': 1383, '跃': 1384, '静': 1385, '柳': 1386, '凰': 1387, '潢': 1388, '管': 1389, '效': 1390, '女': 1391, '衣': 1392, '柜': 1393, '细': 1394, '磁': 1395, '砖': 1396, '伍': 1397, '属': 1398, '浜': 1399, '锈': 1400, '晨': 1401, '漆': 1402, '融': 1403, '互': 1404, '+': 1405, '铃': 1406, '熙': 1407, '滴': 1408, '答': 1409, '消': 1410, '防': 1411, '育': 1412, '母': 1413, '敏': 1414, '架': 1415, '被': 1416, '滑': 1417, '监': 1418, '控': 1419, '淌': 1420, '卷': 1421, '闸': 1422, '资': 1423, '淘': 1424, '娃': 1425, '落': 1426, '朕': 1427, '遵': 1428, '雷': 1429, '照': 1430, '爵': 1431, '苞': 1432, '垭': 1433, '宋': 1434, '榨': 1435, '狐': 1436, '狸': 1437, '磊': 1438, '涂': 1439, '从': 1440, '继': 1441, '俬': 1442, '做': 1443, '橱': 1444, '航': 1445, '针': 1446, '织': 1447, '殡': 1448, '葬': 1449, '折': 1450, '扣': 1451, '俊': 1452, '才': 1453, '团': 1454, '讯': 1455, '拼': 1456, '匹': 1457, '允': 1458, '依': 1459, '雲': 1460, '禄': 1461, '铸': 1462, '沾': 1463, '绿': 1464, '迦': 1465, '完': 1466, '袭': 1467, '暖': 1468, '翼': 1469, '换': 1470, '戴': 1471, '价': 1472, '魅': 1473, '蔻': 1474, '超': 1475, '鞍': 1476, '周': 1477, 'P': 1478, 'p': 1479, '屈': 1480, '臣': 1481, '窖': 1482, 'B': 1483, '钜': 1484, '玩': 1485, '免': 1486, '税': 1487, '旧': 1488, '魔': 1489, '梁': 1490, '隐': 1491, '形': 1492, '积': 1493, '梭': 1494, '彪': 1495, '塞': 1496, '医': 1497, '灵': 1498, '犬': 1499, '仁': 1500, '咨': 1501, '询': 1502, '接': 1503, '待': 1504, '票': 1505, '寄': 1506, '存': 1507, '辛': 1508, '喇': 1509, '叭': 1510, '始': 1511, '郭': 1512, '移': 1513, '复': 1514, '递': 1515, '责': 1516, '燃': 1517, '瀑': 1518, '兽': 1519, '充': 1520, '墓': 1521, '械': 1522, '租': 1523, '赁': 1524, '残': 1525, '疾': 1526, '辅': 1527, '谐': 1528, '所': 1529, '相': 1530, '枪': 1531, '艇': 1532, '挖': 1533, '掘': 1534, '翻': 1535, '殖': 1536, '演': 1537, '洗': 1538, '命': 1539, '念': 1540, '像': 1541, '局': 1542, '蒂': 1543, '纱': 1544, '刷': 1545, '络': 1546, '坻': 1547, '拨': 1548, '均': 1549, '各': 1550, '承': 1551, '轿': 1552, '校': 1553, '芽': 1554, '圈': 1555, '胎': 1556, '检': 1557, '测': 1558, '穿': 1559, '类': 1560, '郊': 1561, '野': 1562, '吸': 1563, '汪': 1564, '沈': 1565, '缝': 1566, '党': 1567, '湿': 1568, '证': 1569, '庙': 1570, '横': 1571, '耀': 1572, '片': 1573, '崇': 1574, '览': 1575, '视': 1576, '推': 1577, '荐': 1578, '取': 1579, '坝': 1580, '坪': 1581, '划': 1582, '漂': 1583, '净': 1584, '吕': 1585, '未': 1586, '岩': 1587, '昂': 1588, '输': 1589, '队': 1590, '拓': 1591, '阮': 1592, '碑': 1593, '坛': 1594, '韵': 1595, '液': 1596, '厚': 1597, '雕': 1598, '刻': 1599, '宽': 1600, '曦': 1601, '轴': 1602, '央': 1603, '护': 1604, '型': 1605, '焊': 1606, '兄': 1607, '弟': 1608, '备': 1609, '纫': 1610, '妻': 1611, '注': 1612, '仝': 1613, '晟': 1614, '喷': 1615, '绘': 1616, '赢': 1617, '坟': 1618, '孵': 1619, '冲': 1620, '拱': 1621, '舞': 1622, '铨': 1623, '蔓': 1624, '痘': 1625, '祛': 1626, '构': 1627, '眉': 1628, '系': 1629, 'R': 1630, '琪': 1631, '肤': 1632, '审': 1633, '耳': 1634, '剪': 1635, '咔': 1636, '嚓': 1637, 'f': 1638, '付': 1639, '指': 1640, 'G': 1641, 'Y': 1642, '脱': 1643, '盖': 1644, '减': 1645, '渼': 1646, '绮': 1647, '萝': 1648, '睫': 1649, '查': 1650, '刺': 1651, '咿': 1652, '朴': 1653, '伽': 1654, '奕': 1655, '整': 1656, '纹': 1657, '羽': 1658, '肌': 1659, '芥': 1660, '知': 1661, '蓓': 1662, '骑': 1663, 'd': 1664, '黛': 1665, '芙': 1666, '漪': 1667, '梵': 1668, '瘦': 1669, '娇': 1670, '睦': 1671, 'ネ': 1672, 'イ': 1673, 'ル': 1674, 'ま': 1675, 'つ': 1676, 'げ': 1677, 'サ': 1678, 'ロ': 1679, 'ン': 1680, '俩': 1681, 'm': 1682, '凌': 1683, '绽': 1684, '放': 1685, '诱': 1686, '惑': 1687, '顾': 1688, '染': 1689, '映': 1690, '婼': 1691, '函': 1692, '疗': 1693, '妍': 1694, '萱': 1695, '沁': 1696, '坐': 1697, '鸥': 1698, '目': 1699, '施': 1700, '橙': 1701, '槿': 1702, '觉': 1703, 'Z': 1704, '享': 1705, '璞': 1706, '苗': 1707, '性': 1708, '纤': 1709, '恢': 1710, '斑': 1711, '丫': 1712, '闺': 1713, '妖': 1714, '概': 1715, '齐': 1716, 'w': 1717, '乙': 1718, '丙': 1719, '芹': 1720, '菊': 1721, '幻': 1722, '莹': 1723, '妞': 1724, '馥': 1725, '倾': 1726, '迟': 1727, '娅': 1728, '短': 1729, '媛': 1730, '膏': 1731, '砭': 1732, '再': 1733, '抗': 1734, '衰': 1735, '凹': 1736, '凸': 1737, '酷': 1738, '仠': 1739, '按': 1740, '霖': 1741, '蹈': 1742, 'Q': 1743, '足': 1744, '蹦': 1745, '床': 1746, '侦': 1747, '绎': 1748, '唱': 1749, '颂': 1750, '贩': 1751, 'v': 1752, '汗': 1753, '戏': 1754, '剧': 1755, 'X': 1756, '偶': 1757, '淀': 1758, '谊': 1759, '搜': 1760, '毕': 1761, '蜗': 1762, '榭': 1763, '滕': 1764, '鬼': 1765, '起': 1766, '酥': 1767, '昔': 1768, '棋': 1769, '版': 1770, '岁': 1771, 'D': 1772, '沽': 1773, '狄': 1774, '叔': 1775, '浅': 1776, '娱': 1777, '逃': 1778, '莓': 1779, '由': 1780, '叙': 1781, '董': 1782, '晤': 1783, '桌': 1784, '棕': 1785, '榈': 1786, '赐': 1787, '盘': 1788, '泳': 1789, '耘': 1790, '坚': 1791, '稻': 1792, '缪': 1793, '狼': 1794, '迈': 1795, '幕': 1796, '浮': 1797, '愿': 1798, '饶': 1799, '釜': 1800, '沃': 1801, '散': 1802, '瑙': 1803, '乔': 1804, '案': 1805, '鲸': 1806, '°': 1807, '澡': 1808, '炼': 1809, '觅': 1810, '雯': 1811, '竞': 1812, '盲': 1813, '筋': 1814, '苹': 1815, '翟': 1816, '玮': 1817, '转': 1818, '阴': 1819, '告': 1820, '诉': 1821, '突': 1822, '破': 1823, '匡': 1824, '芯': 1825, '峦': 1826, '令': 1827, '桑': 1828, '岗': 1829, '硬': 1830, '赌': 1831, '股': 1832, '婉': 1833, '庇': 1834, '历': 1835, '险': 1836, '鸽': 1837, '狗': 1838, '耶': 1839, '稣': 1840, '勃': 1841, '试': 1842, '缆': 1843, '“': 1844, '捍': 1845, '呐': 1846, '咓': 1847, '熔': 1848, '氧': 1849, '啃': 1850, '嘎': 1851, '引': 1852, '拳': 1853, '练': 1854, '提': 1855, '泡': 1856, '浩': 1857, '猎': 1858, '射': 1859, '箭': 1860, '附': 1861, '击': 1862, '剑': 1863, '拜': 1864, '训': 1865, '跆': 1866, '钓': 1867, '溜': 1868, '踢': 1869, '弎': 1870, '搏': 1871, '榆': 1872, '澎': 1873, '湃': 1874, '篮': 1875, '乒': 1876, '响': 1877, '翱': 1878, '澧': 1879, '弹': 1880, '乓': 1881, '柔': 1882, '矿': 1883, '蔚': 1884, '跑': 1885, '纽': 1886, '茸': 1887, '瑛': 1888, '壁': 1889, '锐': 1890, '战': 1891, '径': 1892, '末': 1893, '巅': 1894, '软': 1895, '费': 1896, '模': 1897, 'π': 1898, '稀': 1899, '兀': 1900, '黔': 1901, '肖': 1902, '栋': 1903, '碚': 1904, '捌': 1905, '组': 1906, '鹰': 1907, '功': 1908, '绩': 1909, '遥': 1910, '麟': 1911, '婷': 1912, '帛': 1913, '瑷': 1914, '妤': 1915, '闳': 1916, '辰': 1917, '释': 1918, '员': 1919, '弘': 1920, '毅': 1921, '等': 1922, '舱': 1923, '扑': 1924, '找': 1925, '（': 1926, '）': 1927, '逆': 1928, '幼': 1929, '亲': 1930, '悟': 1931, '袍': 1932, '培': 1933, '牤': 1934, '获': 1935, '垂': 1936, '播': 1937, '鋆': 1938, '埔': 1939, '琛': 1940, '滘': 1941, '龄': 1942, '/': 1943, '职': 1944, '治': 1945, '涌': 1946, '驾': 1947, '考': 1948, '砺': 1949, '刃': 1950, '茁': 1951, '洼': 1952, '挥': 1953, '委': 1954, '践': 1955, '肿': 1956, '瘤': 1957, '言': 1958, '对': 1959, '媒': 1960, '禁': 1961, '毒': 1962, '勘': 1963, '究': 1964, '耿': 1965, '冶': 1966, '遗': 1967, '频': 1968, '翁': 1969, '崖': 1970, '驶': 1971, '榜': 1972, '蕓': 1973, '帮': 1974, '课': 1975, '夹': 1976, '招': 1977, '芦': 1978, '蔡': 1979, '岳': 1980, '酄': 1981, '蕊': 1982, '段': 1983, '赫': 1984, '郑': 1985, '姚': 1986, '严': 1987, '尽': 1988, '萧': 1989, '菁': 1990, '翌': 1991, '曙': 1992, '报': 1993, '斓': 1994, '讲': 1995, '盐': 1996, '稚': 1997, '佘': 1998, '沿': 1999, '违': 2000, '珊': 2001, '苍': 2002, '乖': 2003, '耕': 2004, '读': 2005, '鼠': 2006, '应': 2007, '扶': 2008, '贫': 2009, '帅': 2010, '盒': 2011, '追': 2012, '蔈': 2013, '丘': 2014, '汾': 2015, '楠': 2016, '拇': 2017, '使': 2018, '盾': 2019, '爽': 2020, '导': 2021, '潜': 2022, '算': 2023, '绍': 2024, '卿': 2025, '倪': 2026, '苇': 2027, '扩': 2028, '含': 2029, '蝴': 2030, '结': 2031, '缤': 2032, '纷': 2033, '恭': 2034, '禹': 2035, '宣': 2036, '衍': 2037, '谣': 2038, '骁': 2039, '愚': 2040, '皕': 2041, '闻': 2042, '摆': 2043, '湉': 2044, '葫': 2045, '篱': 2046, '鳌': 2047, '憬': 2048, '喬': 2049, '尧': 2050, '昊': 2051, '鹤': 2052, '淳': 2053, '尤': 2054, '涵': 2055, '衫': 2056, '贻': 2057, '灿': 2058, '垌': 2059, '若': 2060, '晔': 2061, '宸': 2062, '骄': 2063, '识': 2064, '懿': 2065, '窝': 2066, '颖': 2067, '翊': 2068, '祎': 2069, '甄': 2070, '抖': 2071, '佩': 2072, '采': 2073, '过': 2074, '策': 2075, '炬': 2076, '漠': 2077, '旷': 2078, '寻': 2079, '汐': 2080, '邑': 2081, '埃': 2082, '貅': 2083, '么': 2084, '璨': 2085, '珏': 2086, '魄': 2087, '琨': 2088, '湛': 2089, '菩': 2090, '藏': 2091, '拍': 2092, '焦': 2093, '铎': 2094, '竖': 2095, '坦': 2096, '今': 2097, '了': 2098, '烁': 2099, '秭': 2100, '括': 2101, '次': 2102, '锜': 2103, '煊': 2104, '蝙': 2105, '蝠': 2106, '鸾': 2107, '略': 2108, '省': 2109, '傲': 2110, '驻': 2111, '誉': 2112, '欲': 2113, '警': 2114, '支': 2115, '签': 2116, '徽': 2117, '辽': 2118, '甘': 2119, '肃': 2120, '腔': 2121, '诊': 2122, '胸': 2123, '肛': 2124, '枢': 2125, '献': 2126, '灸': 2127, '贞': 2128, '檀': 2129, '闵': 2130, '岔': 2131, '蜂': 2132, '涧': 2133, '滋': 2134, '肩': 2135, '颈': 2136, '腰': 2137, '孟': 2138, '疫': 2139, '雍': 2140, '滦': 2141, '召': 2142, '祝': 2143, '侣': 2144, '伶': 2145, '旦': 2146, '征': 2147, '滧': 2148, '梯': 2149, '参': 2150, '病': 2151, '鼻': 2152, '喉': 2153, '磺': 2154, '奎': 2155, '位': 2156, '厢': 2157, '尿': 2158, '鄂': 2159, '否': 2160, '伤': 2161, '缸': 2162, '窑': 2163, '裴': 2164, '痛': 2165, '录': 2166, '淑': 2167, '贾': 2168, '症': 2169, '断': 2170, '炫': 2171, '隋': 2172, '畅': 2173, '辆': 2174, '圳': 2175, '瓶': 2176, '改': 2177, '膜': 2178, '乘': 2179, '孚': 2180, '毂': 2181, '钊': 2182, '昇': 2183, '钣': 2184, '痕': 2185, '冀': 2186, '郝': 2187, '续': 2188, '伸': 2189, '其': 2190, '昱': 2191, '纵': 2192, '葆': 2193, '嗨': 2194, '眷': 2195, '镀': 2196, '钥': 2197, '匙': 2198, '固': 2199, '虫': 2200, '蜡': 2201, '骆': 2202, '驼': 2203, '异': 2204, '仆': 2205, '妙': 2206, '占': 2207, '珑': 2208, '蓄': 2209, '蒋': 2210, '叉': 2211, '奔': 2212, '钦': 2213, '压': 2214, '挡': 2215, '渤': 2216, '昕': 2217, '贷': 2218, '款': 2219, '烽': 2220, '列': 2221, '署': 2222, '跨': 2223, '己': 2224, '驳': 2225, '缩': 2226, '篷': 2227, '终': 2228, '狂': 2229, '：': 2230, '財': 2231, '廣': 2232, '聘': 2233, ':': 2234, '镖': 2235, '激': 2236, '止': 2237, '编': 2238, '進': 2239, '市': 2240, '键': 2241, '拌': 2242, '在': 2243, '值': 2244, '感': 2245, '馈': 2246, '入': 2247, '们': 2248, '盟': 2249, '寝': 2250, '负': 2251, '、': 2252, '楽': 2253, '，': 2254, '畸': 2255, '侧': 2256, '咱': 2257, '乳': 2258, '套': 2259, '泼': 2260, '炒': 2261, '史': 2262, '稞': 2263, '酩': 2264, '馏': 2265, '醇': 2266, '醋': 2267, '区': 2268, '势': 2269, '搞': 2270, '；': 2271, '辑': 2272, 'z': 2273, '让': 2274, '脸': 2275, '纠': 2276, '错': 2277, '陪': 2278, '烹': 2279, '饪': 2280, '更': 2281, '晒': 2282, '遮': 2283, '努': 2284, '迹': 2285, '奖': 2286, '敬': 2287, '此': 2288, '仞': 2289, '降': 2290, '呢': 2291, '叫': 2292, '?': 2293, '预': 2294, '背': 2295, '靠': 2296, '块': 2297, '烩': 2298, '門': 2299, '即': 2300, '隔': 2301, '豫': 2302, '戒': 2303, '髙': 2304, '逢': 2305, '抵': 2306, '押': 2307, '担': 2308, '请': 2309, '孤': 2310, '困': 2311, '县': 2312, '萬': 2313, '低': 2314, '往': 2315, '诞': 2316, '"': 2317, '炎': 2318, '！': 2319, '寳': 2320, '榮': 2321, '運': 2322, '给': 2323, '您': 2324, '!': 2325, '卧': 2326, '睬': 2327, '买': 2328, '仅': 2329, '硅': 2330, '藻': 2331, '泥': 2332, '著': 2333, '腻': 2334, '嵌': 2335, '貌': 2336, '厉': 2337, '抢': 2338, '切': 2339, '兼': 2340, '椅': 2341, '臭': 2342, '漏': 2343, '鸳': 2344, '鸯': 2345, '要': 2346, '牧': 2347, '侯': 2348, '镶': 2349, '捡': 2350, '臊': 2351, '滚': 2352, '碱': 2353, '哨': 2354, '钠': 2355, '祖': 2356, '葱': 2357, '涤': 2358, '仰': 2359, '核': 2360, '显': 2361, '馄': 2362, '饨': 2363, '棘': 2364, '忧': 2365, '尕': 2366, '肺': 2367, '择': 2368, '韬': 2369, '挂': 2370, '州': 2371, '镯': 2372, '褔': 2373, '评': 2374, '估': 2375, '彻': 2376, '近': 2377, '奋': 2378, '夺': 2379, '决': 2380, '走': 2381, ';': 2382, '址': 2383, '噪': 2384, '增': 2385, '娌': 2386, '崡': 2387, '澶': 2388, 'у': 2389, '尯': 2390, '钃': 2391, '濆': 2392, 'ぉ': 2393, '鍐': 2394, '欑': 2395, '湡': 2396, '鍠': 2397, '粯': 2398, '鏉': 2399, '″': 2400, '箙': 2401, '闆': 2402, '曞': 2403, '埢': 2404, '闂': 2405, 'ㄥ': 2406, 'ご': 2407, '鐏': 2408, '\ue21c': 2409, '\ue188': 2410, '封': 2411, '汴': 2412, '號': 2413, '娓': 2414, '呯': 2415, '鍥': 2416, '炴': 2417, '棌': 2418, '伞': 2419, '浇': 2420, '逍': 2421, '纬': 2422, '鍙': 2423, 'ゅ': 2424, '尰': 2425, '兑': 2426, '栏': 2427, '撤': 2428, '榴': 2429, '砀': 2430, '尝': 2431, '氙': 2432, '倒': 2433, '箱': 2434, '他': 2435, '桶': 2436, '擀': 2437, '馍': 2438, '杖': 2439, '每': 2440, '疼': 2441, '浆': 2442, '棍': 2443, '绯': 2444, '栨': 2445, '灉': 2446, '鎵': 2447, '彂': 2448, '瓒': 2449, '呭': 2450, '競': 2451, '鏍': 2452, '洯': 2453, '绂': 2454, '惧': 2455, '爞': 2456, '鍜': 2457, '屽': 2458, '钩': 2459, '芒': 2460, '褰': 2461, '㈣': 2462, '绉': 2463, '佷': 2464, '瀹': 2465, '氬': 2466, '埗': 2467, '濇': 2468, '笣': 2469, '忓': 2470, '悆': 2471, '堕': 2472, 'ズ': 2473, '绾': 2474, '\ue21b': 2475, '墜': 2476, 'ラ': 2477, '滤': 2478, '变': 2479, '涡': 2480, '携': 2481, '驴': 2482, '钉': 2483, '层': 2484, '纰': 2485, 'ф': 2486, '娴': 2487, '睜': 2488, '鹃': 2489, '\ue6eb': 2490, '刹': 2491, '赠': 2492, '损': 2493, '坏': 2494, '烙': 2495, '穆': 2496, '鱿': 2497, '浣': 2498, '\ue196': 2499, '糊': 2500, '扫': 2501, '凭': 2502, '页': 2503, '详': 2504, '喝': 2505, '饹': 2506, '歡': 2507, '臨': 2508, '链': 2509, '裤': 2510, '去': 2511, '除': 2512, '反': 2513, '刊': 2514, '持': 2515, '沥': 2516, '粘': 2517, '填': 2518, '痞': 2519, '晾': 2520, '區': 2521, '钛': 2522, '镁': 2523, '求': 2524, '煦': 2525, '奴': 2526, '仿': 2527, '淋': 2528, '徒': 2529, '覆': 2530, '橡': 2531, '浠': 2532, '垮': 2533, '彜': 2534, '鏈': 2535, 'ㄩ': 2536, '洉': 2537, '促': 2538, '箔': 2539, '氟': 2540, '槽': 2541, '轨': 2542, '愛': 2543, '扔': 2544, '垃': 2545, '圾': 2546, '叠': 2547, '柱': 2548, '倡': 2549, '(': 2550, '泸': 2551, '廿': 2552, '皆': 2553, '册': 2554, '杜': 2555, '赔': 2556, '杭': 2557, '拥': 2558, '插': 2559, '熟': 2560, '拖': 2561, '闉': 2562, '嬫': 2563, '湇': 2564, '鐗': 2565, '还': 2566, '績': 2567, 'ょ': 2568, '瓭': 2569, '鑹': 2570, '湳': 2571, '杩': 2572, '為': 2573, '攣': 2574, '需': 2575, '雏': 2576, '栧': 2577, '暋': 2578, '鑼': 2579, '惰': 2580, '壓': 2581, '椁': 2582, '愬': 2583, '巺': 2584, '㈡': 2585, '埧': 2586, '妫': 2587, '嬬': 2588, '墝': 2589, '鍖': 2590, '帰': 2591, '鍟': 2592, '嗗': 2593, '姟': 2594, '閰': 2595, '掑': 2596, '簵': 2597, '罐': 2598, '懗': 2599, '囬': 2600, 'キ': 2601, '鎬': 2602, '诲': 2603, '壊': 2604, '鐩': 2605, '祰': 2606, '绐': 2607, '楀': 2608, '笜': 2609, '杞': 2610, '\ue21a': 2611, '寘': 2612, '苔': 2613, '粒': 2614, '薯': 2615, '莴': 2616, '苣': 2617, '菠': 2618, '迁': 2619, '鐢': 2620, '茬': 2621, '敵': 2622, '\ue750': 2623, '停': 2624, '皢': 2625, '戝': 2626, '厠': 2627, '涓': 2628, '槍': 2629, '账': 2630, '熏': 2631, '革': 2632, '扇': 2633, '韶': 2634, '撳': 2635, '崠': 2636, '娑': 2637, '櫠': 2638, '栫': 2639, '晫': 2640, '鍚': 2641, '嶇': 2642, '鏃': 2643, 'ョ': 2644, '珛': 2645, '绌': 2646, '皟': 2647, '貢': 2648, '猛': 2649, '塗': 2650, '谅': 2651, '把': 2652, '够': 2653, '鍘': 2654, '傚': 2655, '\ue18d': 2656, '撮': 2657, '攢': 2658, '鐜': 2659, '╁': 2660, '叿': 2661, '绵': 2662, '绒': 2663, '蚕': 2664, '罩': 2665, '杆': 2666, '籽': 2667, '茅': 2668, '猴': 2669, '鍒': 2670, '\ue1be': 2671, '棫': 2672, '鎷': 2673, '旂': 2674, '綈': 2675, '鐑': 2676, '\ue15f': 2677, '暦': 2678, '障': 2679, '藍': 2680, '闪': 2681, '儫': 2682, '嶉': 2683, '厭': 2684, '姘': 2685, '椋': 2686, '熷': 2687, '搧': 2688, '敤': 2689, '鐧': 2690, '捐': 2691, '揣': 2692, '涜': 2693, '仒': 2694, '€': 2695, '鍓': 2696, '\ue045': 2697, '\ue56a': 2698, '圭': 2699, '唬': 2700, '骞': 2701, '憡': 2702, '荤': 2703, '洏': 2704, '蹇': 2705, '\ue0a4': 2706, '収': 2707, '灞': 2708, '曟': 2709, '澘': 2710, '番': 2711, '吾': 2712, '鎴': 2713, '瀷': 2714, '\ue11f': 2715, '濅': 2716, '腑': 2717, '楂': 2718, '樹': 2719, 'ㄨ': 2720, '\ue1d7': 2721, '\ue160': 2722, '嚎': 2723, '钞': 2724, '浼': 2725, '戦': 2726, '棽': 2727, '姩': 2728, '娼': 2729, '\ue1bd': 2730, '祦': 2731, '嬭': 2732, '\ue511': 2733, '閫': 2734, '犲': 2735, '夋': 2736, '槦': 2737, '樼': 2738, '鲤': 2739, '跟': 2740, '棵': 2741, '舌': 2742, '巾': 2743, '槛': 2744, '疏': 2745, '囤': 2746, '赚': 2747, '籍': 2748, '寇': 2749, '缇': 2750, '筝': 2751, '璁': 2752, '㈤': 2753, '\ue635': 2754, '佃': 2755, '瘽': 2756, '灌': 2757, '涔': 2758, '嬪': 2759, 'x': 2760, '歺': 2761, '摊': 2762, '或': 2763, '晰': 2764, '\ue15e': 2765, '浗': 2766, '寤': 2767, '\ue195': 2768, '閾': 2769, '须': 2770, '擒': 2771, '夢': 2772, '窦': 2773, '邂': 2774, '逅': 2775, '鏂': 2776, '囧': 2777, '绀': 2778, '撹': 2779, '偛': 2780, '庢': 2781, '冨': 2782, '枬': 2783, '愪': 2784, '呰': 2785, '￥': 2786, 'ギ': 2787, '闃': 2788, '胯': 2789, '姵': 2790, '纺': 2791, '記': 2792, '旬': 2793, '泪': 2794, '般': 2795, '呵': 2796, '筹': 2797, '蹄': 2798, 'ソ': 2799, '夌': 2800, 'ゥ': 2801, '\ue100': 2802, '鎺': 2803, '崘': 2804, '楄': 2805, '\ue51c': 2806, '碰': 2807, '淇': 2808, '飮': 2809, '悏': 2810, '嘴': 2811, '抓': 2812, '届': 2813, '見': 2814, '尹': 2815, '默': 2816, '尘': 2817, '閲': 2818, '囪': 2819, '僵': 2820, '绁': 2821, '緳': 2822, '姝': 2823, '﹂': 2824, '彿': 2825, '鍏': 2826, '\ue104': 2827, '辫': 2828, 'タ': 2829, '皻': 2830, '閼': 2831, '嶈': 2832, '〃': 2833, '缁': 2834, '慨': 2835, '閽': 2836, '煶': 2837, '敹': 2838, '瀵': 2839, '勫': 2840, '鍌': 2841, '讳': 2842, '箰': 2843, '鐡': 2844, '滃': 2845, '瓙': 2846, '茜': 2847, '璧': 2848, '徃': 2849, '铔': 2850, '硶': 2851, '堢': 2852, '伀': 2853, '撶': 2854, '滅': 2855, '偣': 2856, '矙': 2857, '栓': 2858, '夊': 2859, '叏': 2860, '鍑': 2861, '哄': 2862, '彛': 2863, '浖': 2864, '豚': 2865, '鏄': 2866, '厜': 2867, '湴': 2868, '抽': 2869, '蕉': 2870, '時': 2871, '閣': 2872, '椾': 2873, '\ue21d': 2874, '埅': 2875, '鏁': 2876, '欒': 2877, '洟': 2878, '嗘': 2879, '牎': 2880, '樓': 2881, '已': 2882, '吴': 2883, '創': 2884, '驢': 2885, '骤': 2886, '酬': 2887, '楊': 2888, '銘': 2889, '崍': 2890, '傻': 2891, '论': 2892, '椴': 2893, '滄': 2894, 'Θ': 2895, '\ue162': 2896, '洽': 2897, '剃': 2898, '律': 2899, '妊': 2900, '娠': 2901, '催': 2902, '斤': 2903, '嫁': 2904, '痣': 2905, '难': 2906, '=': 2907, '瘾': 2908, '描': 2909, '': 2910} -------------------------------------------------------------------------------- /test_file.txt: -------------------------------------------------------------------------------- 1 | 1,172,62,1403,1933,1865,388,724 1 2 | 1245,2248,973,1443,395,7,346 0 3 | 77,55,1110,2326 0 4 | 62,180,186,1307,612 1 5 | 1961,2237,2624,277 0 6 | 325,94,684,1820,487,488,838,1960 1 7 | 187,277,1557,874,186,306 1 8 | 352,353,378,379,179,18,111,112,2,3 1 9 | 54,60,119,549,348,51,590 1 10 | 194,749,911,716 0 11 | 502,335,236,186 1 12 | 1,172,28,215,64,65,230,98 1 13 | 196,950,487,488,838,1960,230,662 1 14 | 704,2245,2230,450,423,1943,605,722,1943,429,722,1943 0 15 | 71,2383,2230 0 16 | 204,442,477,232,1178,237,1170,1171,499 1 17 | 231,115,1242,450,287 0 18 | 370,403,127 1 19 | 462,13,463,92,70,2,3 1 20 | 1309,1443,2204,1492,1147 0 21 | 1462,708,146,581,108,1292,7,79,1028 1 22 | 294,295,19,20 1 23 | 665,666,1132,1771,332,359 0 24 | 135,730,2242,403 0 25 | 302,466,2234,326,40,40,160,564,222,222,33,222,0,223,40 0 26 | 62,276,510,307 1 27 | 2092,643,2281,194 0 28 | 223,222,223,223,326,160,0,0 0 29 | 2184,62,1615 0 30 | 684,708,1933,1865,388,1553 1 31 | 70,1744,873,1412,186,306 1 32 | 415,709,915,785,828 1 33 | 79,1383,187,277,1287,1273 1 34 | 395,1115,4,443,718,1247 0 35 | 270,21,334,304,335,257,336,158,337,2,3 1 36 | 302,466,2230,160,33,223,222,33,326,473,40,223,326,33 0 37 | 1296,11,1057,1284 0 38 | 442,500,1417,500,555 1 39 | 224,473,222,223,223 0 40 | 454,205,11 0 41 | 160,222,326,326,33,222,160,492,33,0,224 0 42 | 220,2189,273,884,554,186,306,517 1 43 | 492,326,326,564,326,492,224,222,564,223,223,0 0 44 | 1500,174,89,1311,1364,622,1231 1 45 | 194,137,487,488,838,1960,660,661,230,662 1 46 | 921,276,133 0 47 | 48,1265,135,1731,1147,1301,651 0 48 | 194,300,158,337 0 49 | 1280,1110,808,807,2898,2770,833 0 50 | 682,690,70,303 1 51 | 197,11,454,205,651,115 0 52 | 111,384,2230,572,2149 0 53 | 1256,1695,487,488,838,1960,660,661,230,662 1 54 | 121,263,209,523,314 0 55 | 187,277,843,309 0 56 | 102,1128,792,1129,21,872,2,11 1 57 | 473,326,492,40,1272,1929,127,2022,1344,216,66,296,651 1 58 | 54,1269,127,1271,1272 1 59 | 574,175,1063 0 60 | 2212,1885,763,1607,1608,480 0 61 | 1604,1632,7,696 0 62 | 101,74,70,983,762,762,423 1 63 | 220,1476,58,186,306 1 64 | 510,1261,224,33,33,2492,939,473,326,326 0 65 | 275,94,64,65,2,3 1 66 | 492,223,33,223,40 0 67 | 1,2526,450,62,383,1338,2309,270,587,572 0 68 | 33,222,160,492,326,326 0 69 | 45,46 1 70 | 113,213,612,1277 0 71 | 466,473,263,60 0 72 | 1486,1896,1292 0 73 | 302,466,160,222,223,222,222,33,473,40,223,160,473 0 74 | 204,54,704,2121 1 75 | 160,224,160,40,40,492,33,473,326,33,160 0 76 | 2138,1270,2495,2438,256 0 77 | 252,102,708,1744,582,68,20 1 78 | 62,1164,1387,513,260,498,1103,1753,878,20 1 79 | 352,353,68,20 1 80 | 524,524,1031,1654,45,46 1 81 | 659,1115,1307,3 0 82 | 252,419,1866,1853,582 1 83 | 246,205,180 1 84 | 388,146,1475,2244,2259,796 0 85 | 33,33,66,67,344,345,19,20 1 86 | 920,921,113,78 1 87 | 814,716,792,97,403 0 88 | 2258,408,1402 0 89 | 866,21,569 0 90 | 222,222,160,40,326,223,0 0 91 | 160,40,40 0 92 | 11,727,2261,716 0 93 | 1977,2233,1829,2156 0 94 | 121,191,961,828,256 1 95 | 25,26,704,1239,423,931,935 0 96 | 293,203,54,363,700,179,18 1 97 | 97,403,2261,348,1337 0 98 | 194,920,397,194,651,68,20 1 99 | 15,74,64,65,487,488,838,1960 1 100 | 357,536,361,2325,357,2074,2908 0 101 | 70,1570,186,388 1 102 | 1401,88,612,1277 1 103 | 715,1946,335,416,1780,151,71 1 104 | 29,650,307,48,240,1307,3 1 105 | 1013,1685,314 0 106 | 25,26,882,845,487,488,838,1960,660,661,230,662 1 107 | 672,362,2,3 1 108 | 108,841,1057,384,1630,1251,250,1704,158,283,194,1124,68,20,34,1656,1103,263,472 1 109 | 302,466,2230,160,224,160,40,492,223,222,0,223,224,0 0 110 | 1296,11,1057,1284 0 111 | 85,734,397,881,1605 1 112 | 1305,1780,241 1 113 | 1475,1154,50,51,628,651,392 1 114 | 55,620,761,306,563,796,797 1 115 | 85,713,1448,1449,1292,7,1301,651 1 116 | 223,223,224,160,223,33,223,33 0 117 | 374,335,79,232,132,1844,150,1722,575,1248,1356,1537 1 118 | 519,528,2,3 1 119 | 206,384,2234,423,2870 0 120 | 641,564,1772,2268,153,222,641 0 121 | 1495,531,70,1622,540 1 122 | 641,160,2268,160,473,681,222,160,224,237 0 123 | 286,205,574,590,250,420,796,957 1 124 | 391,517,260,689,1033,1284,1302 0 125 | 54,77,203,1828,840,1236 1 126 | 1296,2383,2230 0 127 | 263,263,220 0 128 | 181,1265,1297,181,261,140,1231,91,681,160,237 0 129 | 1,205,853,1392,3 1 130 | 324,58,606,76,682,1759,68 1 131 | 25,26,1760,575,506,180 1 132 | 357,1685,70,473,473,987 0 133 | 413,237,1269,625,146,581,20 1 134 | 438,357,357,179,18,2,3 1 135 | 171,171,637,1412 1 136 | 423,496,487,55,181,182,2,3 1 137 | 77,379,2,3 1 138 | 227,942,558 0 139 | 48,899,838,150 1 140 | 1385,59,1693,741,250,1630,641,614,1743,579,1251,248,741,1154,1478,641 1 141 | 66,1475,1308,1028 0 142 | 139,59,120,19,20 1 143 | 222,223,160,231 1 144 | 11,727,716 0 145 | 423,1374,1126,179,1142,1278,3 1 146 | 293,203,83,239,11,958,1265,1297,180 1 147 | 1530,312,672,11,824,403 0 148 | 1445,271,696,1497,724 1 149 | 712,1976,2438 0 150 | 700,82,187,277,1230,205,1231 1 151 | 1908,877,1118,522 0 152 | 382,77,1658,1191,1200,20 1 153 | 157,188,101,388,1553 1 154 | 121,58,23,144 1 155 | 554,795,643,363,279,186,306 0 156 | 1656,873,1287,1364 0 157 | 111,384,2230,325,749,715,1629,2221 0 158 | 1415,86,712 0 159 | 294,481,179,1402 0 160 | 736,720,2362,2363 0 161 | 2261,716,803,716 0 162 | 4,64,30,31 0 163 | 330,391,256 0 164 | 608,273,657,186,306,1929,127,18 1 165 | 450,1143,187,277 1 166 | 1164,180,114,748,828,793,1142,11 1 167 | 1362,1363,1352,793,66,3 1 168 | 1708,75,2380,390,1539,1125 0 169 | 22,208,179,18 1 170 | 352,353,400,351,335,2,3 1 171 | 489,208,1268,1269,619,18 1 172 | 302,466,2230 0 173 | 830,712,828 0 174 | 11,290,643,713,287,15,1430 0 175 | 676,520,261,301,447,1233,1542 1 176 | 368,363,249,946,743,1153,739,1152,997,1153,737,37,35 1 177 | 2199,1511,2312,2192,146,66,449,660,661,230,662 0 178 | 1451,726,1767,712 0 179 | 2411,2434,408,807 0 180 | 1429,1458,270,64,1307 1 181 | 1065,554,2230,160,222,326,160,0,223,0,224,33,326,33 0 182 | 91,5,142,287,714,715,3 1 183 | 118,1443,118,1278 0 184 | 1065,554,2382,160,33,40,326,222,33,224,223,40,33,33 0 185 | 423,496,30,31,2,3 1 186 | 1079,44,187,1364 1 187 | 677,1,132 1 188 | 212,828,1334 0 189 | 641,160,2268,160,160,681,473,492,222,641,237 0 190 | 1,172,1459,269,298,187,449 1 191 | 64,11,1142,208 0 192 | 162,1443,111 0 193 | 1170,1171,277,1172,702,70,208,1065,348,500,862,3 1 194 | 1368,1384,487,488,838,1960 1 195 | 792,162,2243,391,1766 0 196 | 157,1069,7,1072,348,110,1233,3 1 197 | 186,230,637,1412,448,92,388,1293,186,306 1 198 | 40,473,224 0 199 | 423,496,186,261,161,162,2,3 1 200 | 55,138,66,67,19,20 1 201 | 15,1697,1328,651,1605,390,1019 1 202 | 344,345,1784,441 0 203 | 672,362,70,1224,45,535,577,92,1338 1 204 | 270,21,186,1497,1307,70,388,1861,1398,1980,210,186,121,1497,2031,197,1497,724 1 205 | 436,107,660,624 0 206 | 157,735,624,625 0 207 | 1078,616,70,388,64,65,843,731,1294,1233,1294 1 208 | 1065,554,2234,160,222,223,224,222,0,223,33,0,223,33 0 209 | 191,115,643,145,1604,34,1300,622,1605,217,1392 1 210 | 423,496,294,93,2,3 1 211 | 2080,246,487,488,838,1960,660,661,230,662 1 212 | 461,21,335,1125,1249,186,306 1 213 | 184,1078,60,119,487,488,838,1960,660,661,230,662 1 214 | 380,331,66,67,2,3 1 215 | 1092,396,659,1244,554,277,153,312,165,1364,3 1 216 | 101,74,2740,912,792 0 217 | 160,222,223,223,40,40,223,473,0,492,224 0 218 | 461,176,2,3 1 219 | 142,146,70,2,3 1 220 | 26,83,881,1605,194,1124 1 221 | 70,1294,622 0 222 | 492,564,224,643,714,712,2259,796 0 223 | 58,1028,70,266 1 224 | 1296,11,1057,1284 0 225 | 140,296,487,488,186,306 1 226 | 586,198,194,651,78,260,1154,1478,641 1 227 | 665,1617,584,182,102,377 0 228 | 651,1605,609,1185,1122 0 229 | 608,1297,1147,1337,1287,1311,70,4 0 230 | 1477,146,146,1376,596,293,1478,37,1479,946,37,1152,35,1233,3 1 231 | 641,425,51,425,2262,1253,269 0 232 | 1635,392,643,146 1 233 | 676,531,194,793,180,454,667,2324 0 234 | 194,199,252,1313 0 235 | 71,2383,641,224,2268,160,223,681,492,492,223,237 0 236 | 1132,115,1242,287,102 0 237 | 160,224,160,222,33,40,473,0,224,160,33 0 238 | 194,120,70,2,572 1 239 | 252,1180,307,392,393,1933,1865,186,306 1 240 | 75,76,77,78,80,81,2,3 1 241 | 641,473,2268,160,160,681,223,160,326,237 0 242 | 54,832,70,861 0 243 | 423,496,173,181,2,3 1 244 | 210,184,1228,299 1 245 | 442,76,2109,181,325,488,609,660,661,1516,1260,230,662 1 246 | 641,160,2268,160,473,681,222,160,222,641 0 247 | 247,45,535,2505,711,720 0 248 | 1570,1580,587,263 1 249 | 1212,155,194,1124,724 1 250 | 15,92,392,1553 1 251 | 1891,753,1196,197,1125,1249,20 1 252 | 1228,2375,1228 0 253 | 831,640,395,365,877,1200,555 1 254 | 499,1270,263,127,90 1 255 | 2074,273,97,1147 0 256 | 102,377,523,27,665,146,665 0 257 | 222,564,0,1450 0 258 | 25,26,1445,271,1445,1,70,388,1929,127,18 1 259 | 442,456,1287,1311,187,277,1182,376 1 260 | 1,172,1891,882,487,488,838,1960,660,661,230,662 1 261 | 1252,397,549,478,194,1062,194,1649 1 262 | 278,176 1 263 | 61,77,2,3 1 264 | 4,180,1685,939 0 265 | 357,704,357,205 0 266 | 28,146,29,479,2,3 1 267 | 1265,610,450,62 0 268 | 268,1287,1301,651 1 269 | 382,64 1 270 | 1249,362,1855,342 0 271 | 154,548,272,194,1062 1 272 | 239,592,487,488,838,1960 1 273 | 270,21,2077,61,487,488,838,1960 1 274 | 207,269,2,3 1 275 | 1550,632,158,1598,1063,1147 0 276 | 1464,954,55,310,612,71,1277 1 277 | 711,712,720 0 278 | 850,2243,384,610 0 279 | 761,558,487,488,838,1960,660,661,230,662 1 280 | 63,77,2,3 1 281 | 160,564,223 0 282 | 356,76,423 0 283 | 293,203,374,335,2,3,564,1538,1065,835 1 284 | 54,335,70,3 0 285 | 1938,529,194,1124,194,873,1031,1654 1 286 | 158,194,911,716 0 287 | 1475,282,416,446,277 0 288 | 223,326,40,33,492,40 0 289 | 302,466,2234,223,33,0,224,222,473,160,473 0 290 | 1551,1503,1122,217,299,609,88,1287,1311 0 291 | 186,140,21,3 0 292 | 181,1265,1297,25,70,203,1926,181,587,203,1927 0 293 | 1,307,2419 0 294 | 213,422,263,388 1 295 | 375,1,290,2,572 1 296 | 535,558,287,70,1307,612 1 297 | 2,59,1486,1896 0 298 | 332,899,279 1 299 | 263,718,719,720 1 300 | 94,585,120 1 301 | 42,882,181,26 1 302 | 334,1023,736 0 303 | 293,203,382,48,2,3 1 304 | 580,82,129,187,277,1364,622 1 305 | 1947,1553,1933,1865 0 306 | 1191,11,381,1303,130,441,186,306 1 307 | 1564,1270,554,1522,1364,1308,1296 1 308 | 1,172,660,163,487,488,838,1960,660,661,230,662 1 309 | 2,419,2555,535 0 310 | 100,181,263,388 1 311 | 771,78 1 312 | 55,2253,392,393,388,1553 0 313 | 194,199,160,326,326 1 314 | 558,548,296,1511 0 315 | 246,21,392,393,1933,1865 1 316 | 1,172,529,146,487,488,838,1960,660,661,230,662 1 317 | 382,713,45,46,684,555 1 318 | 2261,716,803,716,878,726,716,911,716 0 319 | 25,26,121,121,122,2,3 1 320 | 605,722,846 0 321 | 194,64,1345,59,1142,11 0 322 | 1,172,1,506,48,94,64,65,506,180 1 323 | 684,1820,2419,66,1475,1308,939 0 324 | 146,932,1098 0 325 | 54,276,79,1028,66,3 1 326 | 756,363,487,488,838,1960,660,661,230,662 1 327 | 1106,808,1622 0 328 | 621,101,21,872,623,730,403,3 1 329 | 179,18,314 0 330 | 157,277,319,187,277,194,1124,268,67,186,306 1 331 | 326,222,0,160 0 332 | 160,473,222,160,224 0 333 | 326,222,0,160 0 334 | 366,689,1314,643,1919 0 335 | 224,224,0 0 336 | 208,82,487,1273 1 337 | 26,181,1233,1548,186,306 1 338 | 1167,17,5 0 339 | 326,222,0,160,564,224,223,223,223,326,40,223,473 0 340 | 831,1545,135,1731 0 341 | 442,1576,838,1960 1 342 | 160,224,473,40,40,160,224,160,224,0,326 0 343 | 179,179,830 0 344 | 62,194,201,70,2,3 1 345 | 1985,2371,181,1265,1297,181,70,203,140,391,681,223,564,0,237 0 346 | 147,489,1133,187,1364 1 347 | 66,1475,1308,939 0 348 | 1550,632,2261,716 0 349 | 1072,1397,45,46,1125,1249,20 1 350 | 85,456,307,266,11,690,213 0 351 | 70,416,417,570,763 0 352 | 842,427,381,1795,427 1 353 | 25,26,609,610,70,388,1965,790,388,724 1 354 | 2309,270,587 0 355 | 953,76,34,1125,1249,186,306 1 356 | 474,2,66,449 0 357 | 160,326,252 0 358 | 1,172,148,643,487,488,838,1960,660,661,230,662 1 359 | 62,63,246,487,488,838,1960 1 360 | 1977,146,1959,259,2230,263,388,2254,1108,186,2254,395,186 0 361 | 2072,92,487,488,838,1960,230,662 1 362 | 223,492,222 0 363 | 757,522,487,194,793 1 364 | 102,377,523,27 0 365 | 1335,1396,2517,2031,1334 0 366 | 302,466,2234,326,40,40,160,564,222,222,223,40,473,33,0 0 367 | 54,658,540,1200,1876,1881,1200,1784 1 368 | 199,77,2,3 1 369 | 263,94,140 1 370 | 10,11,958,612,716 0 371 | 101,263,1315,872,1263,1316,1301,684,555 1 372 | 802,813,771,612 1 373 | 1007,1288,881,1605 1 374 | 1694,311,194,388,34,570,474,194,1062,194,1649 1 375 | 160,492,0,237 0 376 | 450,287,139,203 0 377 | 612,22 1 378 | 2353,1279,121,314 0 379 | 382,1320 1 380 | 62,208,828,793 1 381 | 335,140,2109,186,1083,135,635,1,417,452 0 382 | 302,466,2230,160,222,224,223,224,33,224,492,224,160,224 0 383 | 321,258,612,1277 1 384 | 326,222,0,160,564,223,33,0,473,160,40,0,160 0 385 | 1309,796,871,302,466,2230,160,33,223,222,0,33,33,223,326,0,0 0 386 | 193,749,814,716,792 0 387 | 54,329,1196,197,66,3 1 388 | 798,1192,44 0 389 | 363,392,60,119,487,488,838,1960 1 390 | 1551,1977,1550,71 0 391 | 323,2249,1345,1147,2230,492,326,326,564,326,473,160,564,223,33,326,40 0 392 | 55,2052,1063,203 0 393 | 270,21,487,392,1167,1770,266,1454,1088,650,68,487,488,838,1960 1 394 | 54,325,1204,403,3 1 395 | 108,1292,79,1028 0 396 | 270,21,1890,574,487,488,838,1960 1 397 | 1065,554,160,222,326,0,222,0,0,326,473 0 398 | 10,11,34,35,36,37 1 399 | 665,708,684,1222,300,1125 0 400 | 952,423,18 1 401 | 492,40,224 0 402 | 229,290,6,581 1 403 | 302,466,2230,33,33,33,160,222,223,40,223 0 404 | 635,385,110,1435 1 405 | 25,26,499,753,779,1497,724 1 406 | 181,2042,487,488,838,1960,230,662 1 407 | 25,26,889,2034,487,488,838,1960,660,661,230,662 1 408 | 352,353,1194,1195,3 0 409 | 188,1825,18 1 410 | 302,466,2230,223,222,473,492,40,473,33,160 0 411 | 665,72,137 0 412 | 79,314,201,416,1249,277,1523,1524,660,661,230,662 1 413 | 302,466,2230 0 414 | 385,75,201,54,832,716,20 1 415 | 335,140,2109 1 416 | 82,188,186,1307,612 1 417 | 1647,2455,2688,2776,2777,2661,2778,931,2688,2498,2779,2780,2750,2781,2571,2620,2406,2688 0 418 | 850,2243,384,610 0 419 | 1077,210,186,306,383,146,724 1 420 | 158,7,736,810,422 0 421 | 1,2526,450,62,383,1338 0 422 | 160,222,40,222,0,33,326,222,222,33,492 0 423 | 111,384,2230,608,1297,450,62 0 424 | 1705,117,187,1364 1 425 | 300,82,72,263,1150,1110,3 1 426 | 2353,1279,186,314 0 427 | 132,115,1784,441,859,39,20 1 428 | 465,246,18,127,465,1,71 1 429 | 352,353,395,232,236,623,730 1 430 | 1033,150,203,1034,792,623,730 1 431 | 160,222,33,222,33,473,326,223,160,40,40 0 432 | 588,584,1,1191,736,1017 1 433 | 1318,255,1497,724,896,333,68,1448,372,20 1 434 | 6,164,110,1596,956,1305,554,2330,2331 0 435 | 1100,86,720 0 436 | 102,210,187,277,1331,1332 1 437 | 228,1633,1301 0 438 | 1054,856,712 0 439 | 1262,2603,2625 0 440 | 473,40,33,326 0 441 | 160,33,237 0 442 | 659,76,859,39 1 443 | 23,358,590,250,420 1 444 | 153,375,199 0 445 | 568,287,808,1082,180,1492,259,3 0 446 | 25,26,296,1685,70,388 1 447 | 71,2383,2382 0 448 | 207,76,1457,427,873,1412,186,306 1 449 | 302,466,160,222,223,160,222,33,473,492,473,222,222 0 450 | 496,121,657,621,25,256,186,306,383,146,1122 1 451 | 1780,2288,270,572 0 452 | 160,222,473,326,222,0,326,473,223,0,33 0 453 | 1142,610,865,1273,3 1 454 | 1159,713,872,875 1 455 | 401,402,403,3 1 456 | 55,1310,699,213,1265,1297 1 457 | 1,45,1658,1191,1200,68,20 1 458 | 1,172,286,360,64,65,506,180 1 459 | 374,255,1,620,68,1529 1 460 | 326,473,160,224 0 461 | 479,362,771,383 1 462 | 1492,259,3,1275,918,237,2230 0 463 | 2258,408,1402 0 464 | 55,1759,1607,1608,60,61,487,488,838,1960,660,661,230,662 1 465 | 473,492,263,60,183,208,3,296,610 0 466 | 667,54,202,665,708,684,1222 0 467 | 549,164,1538,1392 1 468 | 263,1069,1069 1 469 | 813,605,736 0 470 | 311,110,325,855,487,488,838,1960 1 471 | 540,215,232,1240 1 472 | 643,735,1227,1468,1392,1393 0 473 | 1766,662,1017,862 0 474 | 461,45,694,90,1497,1693,186,306 1 475 | 485,346,950,1617,536,704,1593 0 476 | 113,246,2,3 1 477 | 23,24,10,11,2,3 1 478 | 2156,297,307 1 479 | 154,15,477 0 480 | 223,473,224 0 481 | 160,222,492 0 482 | 4,64,1977,66,1345,1147,2234,160,222,40,40,40,33,0,222,326 0 483 | 643,2381,563,889,911 0 484 | 199,255,241 1 485 | 352,353,269,375,92,80,81,2,3 1 486 | 756,730,806,716 0 487 | 55,1270,70,1307,612 1 488 | 1142,610,27,1287,390,1443,2189,2226,2227 0 489 | 2317,2236,645,70,1180,2317 0 490 | 1325,1345,957 0 491 | 293,285,70,659,297,2,3 1 492 | 129,1373,1083,2442 0 493 | 759,21,487,488,838,1960 1 494 | 11,9,1584,59 0 495 | 397,398,70,2,3 1 496 | 1327,201,203,1127,1296 1 497 | 1308,302,2434 0 498 | 49,50,51,52,53,30,31,2,3 1 499 | 605,70,1035,232,623,730 1 500 | 534,215,397,199,2,3 1 501 | 501,74,423,496,2,3 1 502 | 300,548,21,872,623,730,403,3 1 503 | 160,222,326,160,492,224,160,473,160,326,222 0 504 | 79,754,1472,2156 0 505 | 25,26,70,388,64,65,1497,724 1 506 | 71,2383,2230,181,261,140,1231,492,681,160,33,237 0 507 | 1327,201,165,1364,186,306 1 508 | 302,466,2234 0 509 | 160,224,40,33,160,40,160,473,160,223,224 0 510 | 352,353,454,205,263,220,302,506,180 1 511 | 391,146,1617,208 0 512 | 423,496,29,122,2,3 1 513 | 554,703,1929,127,18 1 514 | 54,27,1079,682,70,1307,612 1 515 | 33,160,326,473,160,160,160 0 516 | 263,360,59,1263,1301,651,583,1302 1 517 | 520,1241,263,388 1 518 | 27,1344,625,731 1 519 | 326,224,160,40,564,33,33,160,473,473,40,160,326 0 520 | 54,260,869,730,1118,522,403 0 521 | 54,129,300,1125,1011 0 522 | 1466,194,268,67,186,306 1 523 | 21,140,1139,847 1 524 | 1065,554,302,836,165,1364 0 525 | 194,651,520,48 0 526 | 55,503,487,488,838,1960 1 527 | 158,194,1421,1422,203 1 528 | 1800,58,513,260,1338,20 1 529 | 160,222,222,224 0 530 | 385,2047,175,451,487,488,838,1960 1 531 | 263,306,71,1417 0 532 | 160,33,33,223,326,222,40,0,326,222,0 0 533 | 160,33,326,33,40,0,40,223,40,40,33 0 534 | 25,26,1,290,208,27,1244,1556,3 1 535 | 323,2249,302,466 0 536 | 302,466,2230 0 537 | 1131,115,958,643,1309,1019,609,1185,1122 1 538 | 1316,1316,194,1313 0 539 | 1486,1896,2018,1292 0 540 | 1477,70,146 0 541 | 496,190,21,173,57,543,2,3 1 542 | 1587,59,404 1 543 | 236,48,18,230,1521 1 544 | 6,164,110 0 545 | 1176,1125,78,351,487,488,838,1960,684,1820,1019,1185,1231 1 546 | 263,220,1265,1297,3 1 547 | 486,1060,1063,1716,696,119,622 0 548 | 1085,1391,1392,1393,1142,1278 1 549 | 1,172,186,1497,1307,70,388,340,391,1861,1398,1497,724 1 550 | 2353,1279,121,314 0 551 | 886,887,129,888,889,890,735,711,810,720,172,203,891,11,696,3 1 552 | 1127,1358,390,1443,1296,11,1057,1284 1 553 | 1241,1969,619,657,186,306,1929,127,18 1 554 | 455,22,1538,277 1 555 | 61,34,790,400,881,1605 1 556 | 205,1249,60,119,384,145,1125,1249,186,306 1 557 | 179,18,540,340,391,642 1 558 | 15,88,2,3 1 559 | 199,171,70,1224,1154,1478,641 1 560 | 1771,1893,2229,454 0 561 | 1129,279 1 562 | 881,371,1125,1249,1356,118,381,45,46,186,306 1 563 | 4,443,390,1019 0 564 | 467,244,478,1777,205,555 1 565 | 429,188,237 0 566 | 364,227,487,488,838,1960,660,661,230,662 1 567 | 825,1595,1412,1933 0 568 | 1976,86,858,205,186,306 1 569 | 199,63,194,1124,724 1 570 | 808,1632,1389,622 0 571 | 289,643,1746 0 572 | 1189,522,563,796,797 1 573 | 179,18,696,3 0 574 | 55,218,15,1139,127,1875,1200,1230,205,1231 1 575 | 2302,641,34,40,33,1251,1772,250 0 576 | 225,188,1274,841 0 577 | 186,665,1389,582 1 578 | 1227,351,540,1200 1 579 | 1336,368,1227,45,7 1 580 | 958,643,390,1019,1205,487,548,258 1 581 | 1301,651,66,446 0 582 | 321,761,1142,610,187,277,165,1364,1227,145,30,31 1 583 | 179,18,1126,2 0 584 | 684,101,922,1122 1 585 | 641,579,1154,53 0 586 | 285,536,80 1 587 | 104,11,808,1632,1604,622,68,20 1 588 | 212,828,1334 0 589 | 194,646,194,395,647 1 590 | 418,198 1 591 | 2015,335,186,388 1 592 | 498,748,1120,59 1 593 | 962,842,1302,476,160,160,492,1818,224,224,224,473,223,160,0,473 0 594 | 523,523,27,27,197,11,454 0 595 | 355,1288,473,492,263,60,101,643,1292,7,1305,643,1302,1028,3 1 596 | 2114,288,408 0 597 | 588,584,1828,840 1 598 | 689,1314,348,1028 0 599 | 210,184,70,1307,612 1 600 | 1125,1249,582,555 1 601 | 1256,748,830,1162 1 602 | 923,72,713 1 603 | 113,208,201,612,71,1277 1 604 | 1101,60,184,934,623,714,712 1 605 | 617,772,623,730 1 606 | 293,203,60,61,688 1 607 | 219,2347,71,121,314 0 608 | 842,544,1205,1085,1287 0 609 | 1,172,97,269,153,111,112,230,98 1 610 | 897,1039,270,714,792 1 611 | 814,716,792 0 612 | 1245,205,4,443,390,1019 1 613 | 1287,1311,1147 0 614 | 722,86,736 0 615 | 2433,277,1429,201 0 616 | 79,699,83,449 0 617 | 160,224,473,33,223,33,473,33,326,222,224 0 618 | 1747,622,32,385,226,435,1537,1748,1577,622,20 1 619 | 101,643,1292,7 1 620 | 238,18,80,81,2,3 1 621 | 232,866,831,1337,1142,1278,3 1 622 | 371,7,1828,840 1 623 | 294,2615,286,286,840 0 624 | 86,247,162,861 0 625 | 476,568,704,256,1196,197,268,67,517 1 626 | 1878,240,507,71 1 627 | 529,123,298,1125,1249,68,20 1 628 | 1038,753,760,343 1 629 | 572,2149,2230,160,33,223,222,33,160,326,224,40,492,222 0 630 | 115,536,191,75,365,1444,1393 0 631 | 1327,287,1070,1141,487,488,838,1960 1 632 | 2262,1253,269 0 633 | 537,370,579,641,302,506,180 1 634 | 370,103,70,2,3 1 635 | 352,353,979,336,487,488,838,1960,660,661,230,662 1 636 | 2673,2693,2694 0 637 | 838,156,2230,223,0,326,0,326,0,326,160 0 638 | 33,326,40,326,327,77,2,3 1 639 | 102,77,2,3 1 640 | 2457,2458,2459 0 641 | 933,467,1064,1065 1 642 | 1297,492,681,160,564,160,237 0 643 | 674,310,1,512 1 644 | 160,473,33 0 645 | 199,435,215,392,393,2,3 1 646 | 326,425,40,40 0 647 | 179,573,429,880 1 648 | 219,1663,392,517 1 649 | 71,2383,2230,181,1265,1297,181,2268,25,160,681,160,222,237 0 650 | 194,1124,194,873,186,306 0 651 | 118,119,302,836,1615,1616 1 652 | 650,650,10,702,667,442,456 0 653 | 632,716,1110,86 0 654 | 270,21,841,235,487,488,838,1960 1 655 | 1514,2146,70,388,1861,1398,1956,1957,1497,724 1 656 | 25,26,602,620,1497,724 1 657 | 293,203,102,74,19,20 1 658 | 1079,132,19,20 0 659 | 423,496,329,530,501,213,2,3 1 660 | 2294,390,186 0 661 | 1683,1270,146,651,145,651,20 1 662 | 302,466,2230,160,33,33,222,33,160,224,160,160,492,40 0 663 | 32,117,549 1 664 | 423,496,200,561,2,3 1 665 | 160,473,223,326 0 666 | 70,184,174,199,213,506,180 1 667 | 199,523,793,1379,3 1 668 | 181,342,1488,625,1187,1278 1 669 | 162,300,1288,548 0 670 | 442,76,442,157,487,488,838,1960,660,661,230,662 1 671 | 356,199,1544,487,488,838,1960 1 672 | 382,208,2,3 1 673 | 454,667,184 0 674 | 893,82,285,623,730 1 675 | 2354,86,828 0 676 | 2353,1279,121,314 0 677 | 70,659,467,496,260,563,796,797 1 678 | 21,174,120,340,659,642 1 679 | 1913,147,210,184,441,1789,45,46,68,1529 1 680 | 830,530 1 681 | 1452,385,55,1401,487,488,838,1960 1 682 | 181,1917,441,1849,132 1 683 | 473,222,492,224,223,326,222,40 0 684 | 1658,2663,1416,2664,397,1416 0 685 | 300,435,403,3 1 686 | 580,1310,76,1352,793 1 687 | 70,1788,736 0 688 | 24,205,9 1 689 | 65,850,2255,388,68,68,1919,2254,101 0 690 | 1353,1039,2743 0 691 | 710,714,111,112,763 0 692 | 350,14,905 1 693 | 25,26,83,84,211,1264,487,488,838,1960,230,662 1 694 | 451,65 1 695 | 505,933,487,488,838,1960,660,661,230,662 1 696 | 302,466,2382 0 697 | 2328,391,939 0 698 | 64,793,45,1178,1304,326,160,224,326,40,160,223 0 699 | 101,11,1224,64,65,1249,1185,1865,1854,507,71 1 700 | 473,326,222 0 701 | 2402,2403,2404 0 702 | 71,2383,2230,181,1265,1297 0 703 | 302,466,2230,160,33,473,222,33,492,224,160,326,223,222 0 704 | 223,224,224,0,0,0,473 0 705 | 2669,856,935 0 706 | 263,722,813,193,716,20,213,70,296,610,941,2325,1286,609,1185,2575 0 707 | 13,180,1755,555,302,506,180 1 708 | 118,2538,1284,1472,160,326,297 0 709 | 1477,297,498,70,1307,612 1 710 | 1253,219,2008,146,1307,3 1 711 | 1,172,115,102,187,277,583,1231,1330,660,661,230,662 1 712 | 150,28,187,277,442,76,341,150,448,92,587,1115,268,67,517 1 713 | 222,33,564,223,33,1507,224,326,1233,2762,466,1896 0 714 | 641,222,2268 0 715 | 101,748,782,7 1 716 | 15,271,194,393 1 717 | 194,1712,732,732 0 718 | 25,26,29,658,187,277,268,67,186,306 1 719 | 442,76,48,201,637,1412 1 720 | 252,651,443,651,260,76 1 721 | 70,1216,1392,1444 0 722 | 135,1731,608 0 723 | 160,222,33,222,33,224,473,326,492,33,33 0 724 | 146,956,1063 0 725 | 223,33,223,222,473,223,33,33 0 726 | 974,975,582,34,976,606,977,616,786 1 727 | 62,287,945,66,449,660,661,230,662 0 728 | 423,856,1098 0 729 | 27,306,394,1389,186,306 1 730 | 789,790,13,2240,121,27,2268 0 731 | 612,612,119 0 732 | 210,184,570,763,1977,2233 0 733 | 1908,877,1118,522 0 734 | 391,1922,2286 0 735 | 442,392,487,488,838,1960,660,661,230,662 1 736 | 160,222,473 0 737 | 2317,203,765,659,1110,2317 0 738 | 549,169,1801,194,277,609,319 1 739 | 62,510,241 1 740 | 2206,909,530 0 741 | 335,140,548,184,1430,174,609,88,660,661,230,662 0 742 | 374,446,549 0 743 | 223,492,160,237 0 744 | 187,277,1308,1330,70,4 1 745 | 1098,730,757,758,736 0 746 | 1272,127,828,2178,2252,137,137,1615,495 0 747 | 157,1075,21,859,39 1 748 | 395,1018,127,465,505,506 0 749 | 1063,1598,2230,160,222,326,160,492,223,223,223,326,222,222 0 750 | 62,1280,315,950,1751,260,590,250,420 1 751 | 187,277,1308,1330 1 752 | 111,1787,523,27 0 753 | 54,225,29,2,572 1 754 | 438,427,1875,1200,1658,1191,1200,1865,1854,20 1 755 | 61,679,1859,1860,1230,205,1231 1 756 | 4,64,30,31 0 757 | 15,363,487,488,838,1960 1 758 | 71,121,314 0 759 | 1493,1063,1755,251,1253,1122 1 760 | 153,1288,730,522 0 761 | 270,21,15,1083,187,277,1284,1302,660,661,230,662 1 762 | 115,1911,487,488,838,1960 1 763 | 1744,1693,1227,45 0 764 | 352,353,12,55,66,67,2,3 1 765 | 492,223,326 0 766 | 838,1551,637,1412 1 767 | 870,190,859,39 1 768 | 293,203,165,558,208,175,2,3 1 769 | 25,26,79,188,1500,535,660,661,230,662,70,1307,612 1 770 | 1871,373,1021,1860,1230,205,1231 1 771 | 293,544 1 772 | 2074,2652,623 0 773 | 153,1288,1644,2420,828 0 774 | 25,26,70,1551,158,629,187,277,1231,1330,660,661,230,662 1 775 | 146,581,263,11,302 0 776 | 1,172,205,1263,487,488,838,1960,660,661,230,662 1 777 | 2094,15,487,488,838,1960,660,661,230,662 1 778 | 659,2131,704,383,146,724 1 779 | 270,21,474,612 1 780 | 523,27,66,1228 0 781 | 270,21,340,222,326,187,277,27,4,1557,1558,517 1 782 | 1176,24,748,1116,86,500,862 1 783 | 232,263,1086,45,535,146,581,20 0 784 | 580,385,187,1308 1 785 | 60,61,881,1605 1 786 | 186,261,571,580,581,582,548,99,2,3 1 787 | 1507,160,326,326,939,160,326,326 0 788 | 45,535,80,1287,968,1311 0 789 | 160,40,160 0 790 | 175,451,158,7,130,20 1 791 | 394,220,269,4,108,1019,827,637,20 1 792 | 1975,88,433,655,2230,537,343,2254,1357,388,2254,625,622,2254,488,388,2254,343,487 0 793 | 25,180,314,263,388 1 794 | 137,651,1364,622,1296 1 795 | 204,27,230,1521 1 796 | 2045,1978,397,2252,676,1389,2252,263,1855 0 797 | 448,92,70,97,873,874,3 1 798 | 685,435,18 1 799 | 48,954,336,4,443,1309,1019 1 800 | 224,40,160 0 801 | 641,160,2268,160,473,681,222,222,473,237 0 802 | 102,297,11,1273 1 803 | 164,1970,1929,127,18 1 804 | 186,64,334,1023,736,97,403 0 805 | 128,1170,1125,1249,186,306 1 806 | 197,75,252,277 0 807 | 983,351,478,924,263,657 1 808 | 1429,86,1514,258,1430,1530 1 809 | 270,21,652,1100,487,488,838,1960,660,661,230,662 1 810 | 1890,82,1305,661,487,488,838,1960,660,661,230,662 1 811 | 160,224,40,492,224,222,222,33,222,473,160 0 812 | 55,643,563,319 0 813 | 181,182,1063,1598 0 814 | 91,21,2553,202 0 815 | 140,132,299,64,343,263,388 1 816 | 545,546,2,3 1 817 | 549,438,1121,612 1 818 | 301,447,2238,1216 0 819 | 383,146,835,2301,2170 0 820 | 487,311,66,446 0 821 | 270,21,12,396,1947,1553,1977,146,1294 1 822 | 549,877,549,1017,862 1 823 | 838,1030,735,48 0 824 | 160,224,224 0 825 | 270,21,506,180 1 826 | 270,21,431,210,232,129,1604,622,724 1 827 | 160,224,326,40,222,33,40,40,326,473,224 0 828 | 280,281,282,283,2,3 1 829 | 364,391,791,792,2,572 1 830 | 473,473,326,297,473,223,326,297 0 831 | 27,1208,187,277,1331,1332 1 832 | 391,1337,48,179,1555,853,1392,3 1 833 | 352,353,54,184,1,71,52,52,741,995,738,741,50,1153,739,1152,741,742,37,1664,36,1020,104,651,1605 1 834 | 176,1187,540,644,64,796 1 835 | 666,2023,86 0 836 | 659,779,108,841,988,622,660,661,230,662 1 837 | 2353,1279,121,314 0 838 | 501,311,274,702,70,208,796,797 1 839 | 1142,610,1584,59 0 840 | 380,188,193,716 1 841 | 278,239,660,1302 0 842 | 879,1265,239,1492,259 0 843 | 311,1250,179,18,796,797 1 844 | 350,905,222,40,297 0 845 | 157,457,487,488,838,1960 1 846 | 102,377,523,27 0 847 | 529,82,487,488,838,1960,230,662 1 848 | 302,466,2230,160,222,223,223,222,0,33,33,224,33,40 0 849 | 181,342,587,1065,277 1 850 | 160,222,223,326,222,0,33,326,473,0,223 0 851 | 213,497,487,488,838,1960,660,661,230,662 1 852 | 60,184,1656,1492,194,1124 1 853 | 1305,1260,354,1896,1292 0 854 | 304,181,202,852,179,1372,180 1 855 | 1846,1847,1125,1249,146,581,873,874,20 1 856 | 239,82,1444,1393 0 857 | 340,391,1028,708 0 858 | 473,564,224,326,382 0 859 | 25,55,70,388,1861,1398,1497,724 1 860 | 473,492,263,60,384,610 0 861 | 335,140,70,2268,696,119 0 862 | 423,496,194,571,572,128,129,130,131 1 863 | 2021,1261,1919 0 864 | 487,488,838,1960,1,172,660,661,230,662 1 865 | 205,18,230,98,564,160,160,237,572 1 866 | 1711,220,278,182,45,46 1 867 | 11,658,643,658,650,610,658 0 868 | 263,580,823,736 1 869 | 2353,1279,121,314 0 870 | 1538,2358,1292,7 0 871 | 232,275,712,20 1 872 | 2776,2777,2661,2690,2691,2692 0 873 | 383,146,835,2301,2170 0 874 | 252,1126,252,2 0 875 | 1104,208,175 1 876 | 160,224,40,222,0,223,33,473,492,224,224 0 877 | 164,692,179,18 1 878 | 199,200,2,3 1 879 | 181,55,70,388 1 880 | 423,496,207,55,140,421,2,3 1 881 | 48,55,651,1605,433,434 1 882 | 154,15,1430,155,230,1521 1 883 | 869,1135,782 0 884 | 939,1472,2244,223,40,33,297,390 0 885 | 186,64,643,142,374,446,1895,1330,296,651,186,306 1 886 | 1490,184,748,450,62 1 887 | 40,473,224 0 888 | 201,208,793,7,266,1454 0 889 | 25,26,263,1120,643,487,488,838,1960,660,661,230,662 1 890 | 301,447,2238,1216 0 891 | 1142,1503,609,88 0 892 | 103,531,487,488,838,1960 1 893 | 222,33,222,33,224,40,33,222,473,160 0 894 | 224,52,50,641,1251,1630,1154,641,248,51,614 1 895 | 388,753,186,1603,1503,1504,186,306 1 896 | 302,466,2230,224,223,0,223,473,222,326,160 0 897 | 136,155,845,194,651,433,434 1 898 | 383,146,835 0 899 | 1866,1853,582 0 900 | 4,828,1323 0 901 | 1368,368,392,393,637,1412 1 902 | 15,88,314,315,60,61,2,3 1 903 | 4,5,2,3 1 904 | 54,651,118 1 905 | 364,48,1538,277 1 906 | 94,210,2013,1082,263,388 1 907 | 1202,1401,1622,1742,388,1553 1 908 | 2353,1279,121,314 0 909 | 224,0,223,741,1154,1155,37,1152,36 1 910 | 908,76,115,392,487,488,838,1960 1 911 | 68,1919,1520,2244 0 912 | 302,466,2230 0 913 | 700,456,708,70,1307,612 1 914 | 448,92,91,186 1 915 | 1626,2900,2901,1657 0 916 | 415,390,59,517 1 917 | 2559,642,1353,2021,691 0 918 | 13,140,70,1307,612 1 919 | 296,2411,62,2286 0 920 | 160,33,326,40,224,40,224,223,473,492,0 0 921 | 270,21,140,152,158,7,2,3 1 922 | 25,26,302,86,90,629,388,724 1 923 | 25,26,22,287,456,28,187,277,1284,1302,660,661,1516,1260,230,662 1 924 | 136,397,807,1277,476,1710,1514,186,306 1 925 | 713,176,580,86,1506,1515 1 926 | 2074,273,97,1147 0 927 | 158,194,911,716 0 928 | 8,141,1744,582 1 929 | 666,2073,734,1141,186,64 0 930 | 1489,1391,443 1 931 | 70,1645,1472 0 932 | 194,115,394,127,1529 1 933 | 416,2411,1351 0 934 | 844,232,1180 0 935 | 327,658,748,828,20 1 936 | 352,353,228,650,117,487,488,838,1960 1 937 | 179,18,2,3 1 938 | 2245,923,672,2246 0 939 | 302,466,2230,160,222,224,223,224,33,224,492,224,160,224 0 940 | 1,172,1079,1500,487,488,838,1960,660,661,230,662 1 941 | 160,40,40,297 0 942 | 659,64,563,572 1 943 | 558,548,2518,1566,1334 0 944 | 208,884,277,944,1308,1330,206,384,1231 1 945 | 641,160,2268,160,473,681,222,326,0,237 0 946 | 92,93,111,112,19,20 1 947 | 140,194,59,1693 1 948 | 1724,34,269,153,1664,739,738,1065,609,1111,1015 1 949 | 13,180,704,2121,2122,1529 1 950 | 567,143,487,488,838,1960 1 951 | 580,487,194,1124,724 1 952 | 97,169,96,194,1062,194,1649 1 953 | 48,980,187,268 1 954 | 889,2274,635,1126,1733,270,162,735,2275 0 955 | 1491,1492,1446,2127 0 956 | 160,224,326,473,473,40,326,224,223,492,40 0 957 | 275,563,1511,2356 0 958 | 1083,520,203 0 959 | 713,306,146,702,129,129,329 0 960 | 363,382,259,487,488,838,1960 1 961 | 115,1406,1406,79,1028 1 962 | 287,76,241,1813,643,1740,652,186,306 1 963 | 175,208,859,1184,443 1 964 | 186,64,302,188,48,360,404,384,610,797 1 965 | 2149,1104,1731,906,943,11,219,284 1 966 | 572,270 1 967 | 13,180,1755,555 1 968 | 608,602,853,662 1 969 | 55,181,1180,855,70,388 1 970 | 54,1134,922,2808,2528,34,563,2809 0 971 | 285,923,45,46 1 972 | 70,71,1444,1393 0 973 | 160,224,326,222,33,473,222,0,326,224,492 0 974 | 1707,1497,146,1142,610,1626,1625 1 975 | 54,129,300,1125,1011 0 976 | 25,26,70,225,186,1497,1446,2127,1497,724 1 977 | 270,21,193,57,187,277,165,1364,660,661,230,662 1 978 | 232,71,182 0 979 | 1378,150,365,1538,1392 1 980 | 382,62,133,70,1079,1338,20 1 981 | 349,365,1047,301,447,1529 1 982 | 2130,239,513,523,1472,70,1307,612 1 983 | 102,55,525,21,2,3 1 984 | 71,2383,2234,352,353,716,18,1580,59,1263,2240,555,1098,1352,793,641,2268,160,681,160,40,237 0 985 | 1776,902,344,345 1 986 | 495,462,19,20 1 987 | 85,129,474,104 0 988 | 370,1077,865,138 1 989 | 352,353,426,152,427,80,81,2,3 1 990 | 21,807,806,943,125 0 991 | 263,915,263,830,1095,792,59,931,712 1 992 | 850,2243,384,610 0 993 | 105,1340,792,1292,7,70,4 1 994 | 781,782,528 1 995 | 461,651,1244,1556 1 996 | 1063,25,1604,1632,881,1605 1 997 | 203,3,1187,374,1919,160,326,252 0 998 | 2043,617,643,487,488,838,1960 1 999 | 162,416,2224 0 1000 | 160,222,223,222,222,33 0 1001 | 164,269,428,2,3 1 1002 | 1327,651,1308,1330 1 1003 | 263,587,994,487,488,838,1960 1 1004 | 199,169,549,365,428 1 1005 | 2353,1279,121,314 0 1006 | 263,25,703,79,754,268,67,186,306 1 1007 | 1309,796,302,466,2234,160,33,160,223,0,40,473,33,224,224,223 0 1008 | 334,62,2320,71,321 0 1009 | 94,210,442,208,187,277 1 1010 | 335,140,2109,696,119,622 0 1011 | 473,160,326 0 1012 | 302,466,2230,326,492,224,222,564,223,473,473,160,326,223,33 0 1013 | 160,224,160,40,40,160,326,222 0 1014 | 584,182,865,1017 1 1015 | 71,2383,2230,641,224,2268,160,223,681,492,0,224,237 0 1016 | 102,479,58,187,277,145,1604,186,306 1 1017 | 160,224,473,473,492,40,0,222,326,0,222 0 1018 | 2337,1306,2098,2254,1245,735,1468,1085 0 1019 | 602,658,657,54,83,84,1947,1553,1993,252,1294 1 1020 | 71,608,2234 0 1021 | 222,0,0,160,40 0 1022 | 612,71,1277 1 1023 | 270,21,60,184,1656,1492,299,90,1497,724 1 1024 | 290,651,1216,233,194,793 1 1025 | 813,605,736 0 1026 | 293,203,405,220,68,1777,205,555 1 1027 | 605,722,846 0 1028 | 364,194,11 0 1029 | 2662,1120,922 0 1030 | 480,675,70,388,1861,1398,704,2121,1497,724 1 1031 | 1785,1786,190,859,39,797 1 1032 | 270,21,602,180,716,20 1 1033 | 391,726,839,716 0 1034 | 27,4,1167,704 0 1035 | 2261,935,397 0 1036 | 542,300,824,735,70,1788,736 0 1037 | 196,643,71,1277 1 1038 | 2246,2492,1930,22 0 1039 | 70,290,1492,259 1 1040 | 1302,476,268,67,3 0 1041 | 223,224,224 0 1042 | 224,492,33 0 1043 | 384,610,186 0 1044 | 71,2383,2230,181,1265,1297,91,681,25,568,237 0 1045 | 186,64,135,635,448,92,1050,182,183,208,3 1 1046 | 2353,1279,121,314 0 1047 | 727,188,302,836,1553,1346 1 1048 | 160,326,681,222,223,473,564,222,223,222,237 0 1049 | 442,76,2109,270,1617,487,488,838,1960,660,661,230,662 1 1050 | 305,62,487,488,838,1960 1 1051 | 139,1241,186,388 1 1052 | 1079,188,133,792,1273 1 1053 | 611,155,345,120,471,194,793 1 1054 | 1126,2,79,1028 0 1055 | 1,172,456,198,487,488,838,1960,660,661,230,662 1 1056 | 277,2205,147,1417,635 1 1057 | 469,231,398,1122 1 1058 | 66,1228,266,186 0 1059 | 301,447,2238,1216 0 1060 | 950,86,1277,7 0 1061 | 6,61,194,1391,1287 0 1062 | 102,103,104,18,80,81,2,3 1 1063 | 1,220,699,176,307,1307,3 1 1064 | 58,1133,325,1629,2221,1439 0 1065 | 160,222,223,0,222,492,224,223,0,326,160 0 1066 | 252,220,187,277,194,1124 1 1067 | 844,877,227,187,277,1364,622,517 1 1068 | 332,205,11,884,449,70,797 1 1069 | 602,658,186,306,1553,637,1412,266,1454 1 1070 | 1345,59,795 0 1071 | 89,90,66,67,2,3 1 1072 | 203,610,2234 0 1073 | 1142,610,1364,277 1 1074 | 1384,395,793,7,66,3 1 1075 | 54,772,905,1313 0 1076 | 294,560,64,65,1659,1632,1389,622,186,306 1 1077 | 226,1063,1746 0 1078 | 805,56,596,17,806,807,808,676,58,711,809,714,711,810 1 1079 | 422,657,204,290,256,186,306,383,146,1122 1 1080 | 487,488,1292,7 0 1081 | 1005,155,613,738,194,1062,34,194,1649 1 1082 | 33,222,160,492,326,326 0 1083 | 423,496,557,558,348,2,3 1 1084 | 25,26,90,629,70,388,1,172,388,724,564,137,1547,70,388,180 1 1085 | 216,1344,203,31,1943,1265,253,203,31,1943,383,1338,450,62,1943,1287,1311,450,62,1943,11,1273,450,62 0 1086 | 97,403,1943,1373 0 1087 | 263,48,768,232,623,730 1 1088 | 1550,632,276 0 1089 | 306,1065,2295 0 1090 | 263,181,1279,635,716,179 1 1091 | 194,1124,145,651,145,146 0 1092 | 186,64,2091,388,1074,1964,186,306,25,26,2091,1497,724 1 1093 | 382,259,2281 0 1094 | 79,232,132,70,477 1 1095 | 1496,348,1269,625,476,724,127 1 1096 | 137,1383,187,277,194,1124,1287,1273,3 1 1097 | 186,64,1208,252,66,1328 0 1098 | 1111,1933,1083,988 0 1099 | 659,876,865,877,878,187,21,872 1 1100 | 796,1784,796,2341 0 1101 | 137,1547,584,186 1 1102 | 2474,2475,2476,2062,2477,2473 0 1103 | 499,1916,1142,610,45,46,1031,1654,20 1 1104 | 340,568,5,416,800,796,797 1 1105 | 665,708,684,1222,300,1125,72 0 1106 | 2559,642,1353,2021,691 0 1107 | 73,16,74,2,3 1 1108 | 1550,632,1308,1330 0 1109 | 217,299,968,2334,86 0 1110 | 62,188,374,117 0 1111 | 282,1141,45,46,441,1789 1 1112 | 1345,1157,353,2143,789,790,13,603,2241,531 0 1113 | 186,64,334,62,2010,85,3 0 1114 | 323,160,297 0 1115 | 287,1365,45,708 1 1116 | 263,97,14,11 1 1117 | 302,466,2230,160,222,160,492,326 0 1118 | 70,259,881,1605 1 1119 | 293,203,622,609,388,724,873,1412,20 1 1120 | 293,203,213,83,2,3 1 1121 | 18,314,3 0 1122 | -------------------------------------------------------------------------------- /training.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sharpiless/paddleocr-enterprise-entity-recognition/ff0bf70d9d0d9d21f9449dcf880276d4ee654b74/training.jpg --------------------------------------------------------------------------------