├── README.md └── scut-dlvc.jpeg /README.md: -------------------------------------------------------------------------------- 1 | # Scene Text Recognition Resources 2 | [

Author: 陈晓雪

](https://github.com/CCchenxiaoxue) 3 | The paper "Text Recognition in the Wild: A Survey" (accepted to appear in ACM Computing Surveys) in [arXiv](https://arxiv.org/pdf/2005.03492v3.pdf) version is available now. 4 | 5 | # ❗❗ Newest Version Can be Found Here ❗❗ 6 | ## This repository is no longer maintaining now, and you can refer to our newest one 7 | ## [Scene Text Recognition Recommendations](https://github.com/HCIILAB/Scene-Text-Recognition-Recommendations) 8 | 9 | ## Updates 10 | 11 | Dec 24, 2019: add 20 papers and update corresponding tables. 12 | 13 | Feb 29, 2020: add AAAI-2020 papers and update corresponding tables. 14 | 15 | May 8, 2020: add CVPR-2020 papers and update corresponding tables. 16 | 17 | Dec 8, 2020: add 11 papers and update corresponding tables. You can download the new [Excel](https://pan.baidu.com/s/1xitxu7R5hw27pVV7eJ1c7w) prepared by us. (Password: sj2t) 18 | 19 | *** 20 | 21 | 22 | 23 | - [1. Datasets](#1-datasets) 24 | - [1.1 Regular Latin Datasets](#11-regular-latin-datasets) 25 | - [1.2 Irregular Latin Datasets](#12-irregular-latin-datasets) 26 | - [1.3 Multilingual Datasets](#13-multilingual-datasets) 27 | - [1.4 Synthetic Datasets](#14-synthetic-datasets) 28 | - [1.5 Comparison of the Benchmark Datasets](#15-comparison-of-the-benchmark-datasets) 29 | - [2. Performance Comparison of Recognition Algorithms](#2-performance-comparison-of-recognition-algorithms) 30 | - [2.1 Characteristics Comparison of Recognition Approaches](#21-characteristics-comparison-of-recognition-approaches) 31 | - [2.2 Performance Comparison on Benchmark Datasets](#22-performance-comparison-on-benchmark-datasets) 32 | - [2.2.1 Performance Comparison of Recognition Algorithms on Regular Latin Datasets](#221-performance-comparison-of-recognition-algorithms-on-regular-latin-datasets) 33 | - [2.2.2 Performance Comparison of Recognition Algorithms on Irregular Latin Datasets](#222-performance-comparison-of-recognition-algorithms-on-irregular-latin-datasets) 34 | - [3. Survey](#3-survey) 35 | - [4. OCR Service](#4-ocr-service) 36 | - [5. References](#5-references) 37 | - [6.Help](#6help) 38 | - [7.Copyright](#7copyright) 39 | 40 | 41 | 42 | 43 | ## 1. Datasets 44 | 45 | 46 | ### 1.1 Regular Latin Datasets 47 | 48 | - IIIT5K[31]： 49 | * **Introduction:** The IIIT5K dataset [31] contains 5,000 text instance images: 2,000 for training and 3,000 for testing. It contains words from street scenes and from originally-digital images. Every image is associated with a 50 -word lexicon and a 1,000 -word lexicon. Specifically, the lexicon consists of a ground-truth word and some randomly picked words. 50 | * **Link:** [IIIT5K-download](http://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset) 51 | - SVT[1]： 52 | * **Introduction:** The SVT dataset [1] contains 350 images: 100 for training and 250 for testing. Some images are severely corrupted by noise, blur, and low resolution. Each image is associated with a 50 -word lexicon. 53 | * **Link:** [SVT-download](http://vision.ucsd.edu/~kai/svt/) 54 | - ICDAR 2003(IC03)[33]： 55 | * **Introduction:** The IC03 dataset [33] contains 509 images: 258 for training and 251 for testing. Specifically, it contains 867 cropped text instances after discarding images that contain non-alphanumeric characters or less than three characters. Every image is associated with a 50 -word lexicon and a full-word lexicon. Moreover, the full lexicon combines all lexicon words. 56 | * **Link:** [IC03-download](http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions) 57 | - ICDAR 2013(IC13)[34]： 58 | * **Introduction:** The IC13 dataset [34] contains 561 images: 420 for training and 141 for testing. It inherits data from the IC03 dataset and extends it with new images. Similar to IC03 dataset, the IC13 dataset contains 1,015 cropped text instance images after removing the words with non-alphanumeric characters. No lexicon is associated with IC13 . Notably, 215 duplicate text instance images [65] exist between the IC03 training dataset and the IC13 testing dataset. Therefore, care should be taken regarding the overlapping data when evaluating a model on the IC13 testing data. 59 | * **Link:** [IC13-download](http://dagdata.cvc.uab.es/icdar2013competition/?ch=2&com=downloads) 60 | - SVHN[45]： 61 | * **Introduction:** The SVHN [45] dataset contains more than 600,000 digits of house numbers in natural scenes. It is obtained from a large number of street view images using a combination of automated algorithms and the Amazon Mechanical Turk (AMT) framework. The SVHN dataset was typically used for scene digit recognition. 62 | * **Link:** [SVHN-download](http://ufldl.stanford.edu/housenumbers/) 63 | 64 | 65 | ### 1.2 Irregular Latin Datasets 66 | 67 | - SVT-P[35]： 68 | - **Introduction:** The SVT-P [35] dataset contains 238 images with 639 cropped text instances. It is specifically designed to evaluate perspective distorted text recognition. It is built based on the original SVT dataset by selecting the images at the same address on Google Street View but with different view angles. Therefore, most text instances are heavily distorted by the non-frontal view angle. Moreover, each image is associated with a 50-word lexicon and a full-word lexicon. 69 | - **Link:** [SVT-P-download](https://pan.baidu.com/s/1rhYUn1mIo8OZQEGUZ9Nmrg ) \(Password : vnis) 70 | - CUTE80[36]： 71 | - **Introduction:** The CUTE80 dataset [36] contains 80 high-resolution images with 288 cropped text 72 | instances. It focuses on curved text recognition. Most images in CUTE80 have a complex background, perspective distortion, and poor resolution. No lexicon is associated with CUTE80. 73 | - **Link:** [CUTE80-download](http://cs-chan.com/downloads_CUTE80_dataset.html) 74 | - ICDAR 2015(IC15)[37]： 75 | - **Introduction:** The IC15 dataset [37] contains 1,500 images: 1,000 for training and 500 for testing. 76 | Specifically, it contains 2,077 cropped text instances, including more than 200 irregular text samples. As text images were taken by Google Glasses without ensuring the image quality, most of the text is very small, blurred, and multi-oriented. No lexicon is provided. 77 | - **Link:** [IC15-download](http://rrc.cvc.uab.es/?ch=4&com=downloads) 78 | - COCO-Text[38]： 79 | - **Introduction:** The COCO-Text dataset [38] contains 63,686 images with 145,859 cropped text instances. It is the first large-scale dataset for text in natural images and also the first dataset to annotate scene text with attributes such as legibility and type of text. However, no lexicon is associated with COCO-Text. 80 | - **Link:** [COCO-Text-download](https://vision.cornell.edu/se3/coco-text-2/) 81 | - Total-Text[39]： 82 | - **Introduction:** The Total-Text dataset [39] contains 1,555 images with 11,459 cropped text instance images. It focuses on curved scene text recognition. Images in Total-Text have more than three different orientations, including horizontal, multi-oriented, and curved. No lexicon is associated with Total-Text. 83 | - **Link:** [Total-Text-download](https://github.com/cs-chan/Total-Text-Dataset) 84 | 85 | 86 | ### 1.3 Multilingual Datasets 87 | 88 | - RCTW-17(RCTW competition，ICDAR17)[40]： 89 | - **Introduction:** The RCTW-17 dataset contains 12,514 images: 11,514 for training and 1,000 for testing. Most are natural images collected by cameras or mobile phones, whereas others are digital-born. Text instances are annotated with labels, fonts, languages, etc. 90 | - **Link:** [RCTW-17-download](http://rctw.vlrlab.net/dataset/) 91 | - MTWI(competition)[41]： 92 | - **Introduction:** The MTWI dataset contains 20,000 images. This is the first dataset constructed by Chinese and Latin web text. Most images in MTWI have a relatively high resolution and cover diverse types of web text, including multi-oriented text, tightly-stacked text, and complex-shaped text. 93 | - **Link:** [MTWI-download ](https://pan.baidu.com/s/1SUODaOzV7YOPkrun0xSz6A#list/path=%2F) \(Password:gox9) 94 | - CTW[42]： 95 | - **Introduction:** The CTW dataset includes 32,285 high-resolution street view images with 1,018,402 character instances. All images have character-level annotations: the underlying character, the bounding box, and six other attributes. 96 | - **Link:** [CTW-download](https://ctwdataset.github.io/) 97 | - SCUT-CTW1500[43]： 98 | - **Introduction:** The SCUT-CTW1500 dataset contains 1,500 images: 1,000 for training and 500 99 | for testing. In particular, it provides 10,751 cropped text instance images, including 3,530 with curved text. The images are manually harvested from the Internet, image libraries such as Google Open-Image, or phone cameras. The dataset contains a lot of horizontal and multi-oriented text 100 | - **Link:** [SCUT-CTW1500-download](https://github.com/Yuliang-Liu/Curve-Text-Detector) 101 | * LSVT(LSVT competition, ICDAR2019)[57]: 102 | * **Introduction:** The LSVT dataset contains 20,000 testing samples, 30,000 fully annotated training samples, and 400,000 training samples with weak annotations (i.e., with partial labels). All images are captured from streets and reflect a large variety of complicated real-world scenarios, e.g., store fronts and landmarks. 103 | * **Link:** [LSVT-download](https://rrc.cvc.uab.es/?ch=16&com=downloads) 104 | * ArT(ArT competition, ICDAR2019)[58]: 105 | * **Introduction:** The ArT dataset [58] contains 10,166 images: 5,603 for training and 4,563 for testing. ArT is a combination of Total-Text, SCUT-CTW 1500 , and Baidu Curved Scene Text 4 , which was collected to introduce the arbitrary-shaped text problem. Moreover, all existing text shapes (i.e., horizontal, multi-oriented, and curved) have multiple occurrences in the ArT dataset. 106 | * **Link:** [ArT-download](https://rrc.cvc.uab.es/?ch=16&com=downloads) 107 | * ReCTS-25k(ReCTS competition, ICDAR2019)[59]: 108 | * **Introduction:** The ReCTS-25k dataset [59] contains 25,000 images: 20,000 for training and 5,000 for testing. All the images are from the Meituan-Dianping Group, collected by Meituan business mer- 109 | chants, using phone cameras under uncontrolled conditions. Specifically, ReCTS-25 k dataset mainly contains images of Chinese text on signboards. 110 | * **Link:** [ReCTS-download](https://rrc.cvc.uab.es/?ch=16&com=downloads) 111 | * MLT(MLTcompetition, ICDAR2019) [81]: 112 | * **Introduction:** The MLT-2019 dataset [81] contains 20,000 images: 10,000 for training (1,000 per language) and 10,000 for testing. The dataset includes ten languages, representing seven different scripts: Arabic, Bangla, Chinese, Devanagari, English, French, German, Italian, Japanese, and Korean. The number of images per script is equal. 113 | * **Link:** [MLT-download](https://rrc.cvc.uab.es/?ch=15&com=downloads) 114 | 115 | 116 | ### 1.4 Synthetic Datasets 117 | 118 | * Synth90k [53] : 119 | * **Introduction:** The Synth90k dataset contains 9 million synthetic text instance images from a set of 90k common English words. Words are rendered onto natural images with random transformations and effects, such as random fonts, colors, blur, and noises. Synth90k dataset can emulate the distribution of scene text images and can be used instead of real-world data to train data-hungry deep learning algorithms. Besides, every image is annotated with a ground-truth word. 120 | * **Link:** [Synth90k-download](http://www.robots.ox.ac.uk/~vgg/data/text/) 121 | * SynthText [54] : 122 | * **Introduction:** The SynthText dataset contains 800,000 images with 6 million synthetic text instances. As in the generation of Synth90k dataset, the text sample is rendered using a randomly selected font and transformed according to the local surface orientation. Moreover, each image is annotated with a ground-truth word. 123 | * **Link:** [SynthText-download](https://github.com/ankush-me/SynthText) 124 | * Verisimilar Synthesis [73] : 125 | * **Introduction:** The Verisimilar Synthesis dataset [73] contains 5 million synthetic text instance images. Given background images and source texts, a semantic map and a saliency map are first 126 | determined which are then combined to identify semantically sensible and apt locations for text embedding. The color, brightness, and orientation of the source texts are further determined adaptively according to the color, brightness, and contextual structures around the embedding locations within the background image. 127 | * **Link:** [Verisimilar Synthesis](https://github.com/fnzhan/Verisimilar-Image-Synthesis-for-Accurate-Detection-and-Recognition-of-Texts-in-Scenes) 128 | * UnrealText [88]: 129 | * **Introduction:** The UnrealText dataset [88] contains 600K synthetic images with 12 million cropped text instances. It is developed upon Unreal Engine 4 and the UnrealCV plugin [89]. Text instances are regarded as planar polygon meshes with text foregrounds loaded as texture. These meshes are placed in suitable positions in 3D world, and rendered together with the scene as a whole. The same font set from [Google Fonts](https://fonts.google.com/) and the same text corpus, i.e., Newsgroup20, are used as SynthText does. 130 | * **Link:** [Verisimilar Synthesis](https://github.com/fnzhan/Verisimilar-Image-Synthesis-for-Accurate-Detection-and-Recognition-of-Texts-in-Scenes) 131 | 132 | 133 | ### 1.5 Comparison of the Benchmark Datasets 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 492 | 493 | 494 | 495 | 496 | 497 | 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | 506 | 507 | 508 | 509 | 510 | 511 | 512 | 513 | 514 | 515 | 516 | 517 | 518 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 528 | 529 | 530 | 531 | 532 | 533 | 534 | 535 | 536 | 537 | 538 | 539 | 540 | 541 | 542 | 543 | 544 |

Comparison of the Benchmark Datasets

Datasets

Language

Images

Lexicon

Label

Type

Pictures

Training Pictures

Testing Pictures

Instances

Training Instances

Testing Instances

Full

None

Char

Word

IIIT5K[31]

English

1120

380

740

5000

2000

3000

√

Regular

SVT[32]

English

350

100

250

725

211

514

√

Regular

IC03[33]

English

509

258

251

2268

1157

1111

√

Regular

IC13[34]

English

561

420

141

5003

3564

1439

√

Regular

SVHN[45]

Digits

600000

573968

26032

600000

573968

26032

√

Regular

SVT-P[35]

English

238

639

√

Irregular

CUTE80[36]

English

288

√

Irregular

IC15[37]

English

1500

1000

500

6545

4468

2077

√

Irregular

COCO-Text[38]

English

63686

43686

10000

145859

118309

27550

√

Irregular

Total-Text[39]

English

1555

1255

300

11459

11166

293

√

Irregular

RCTW-17[40]

Chinese/English

12514

11514

1000

√

Regular

MTWI[41]

Chinese/English

20000

10000

290206

141476

148730

√

Regular

CTW[42]

Chinese/English

32285

25887

3269

1018402

812872

103519

√

Regular

SCUT-CTW1500[43]

Chinese/English

1500

1000

500

10751

7683

3068

√

Irregular

LSVT[57], [63]

Chinese/English

450000

30000

20000

√

Irregular

ArT[58]

Chinese/English

10166

5603

4563

98455

50029

48426

√

Irregular

ReCTS-25k[59]

Chinese/English

25000

20000

5000

119713

108924

10789

√

Irregular

MLT[81]

Multilingual

20000

10000

191639

89177

102462

√

Irregular

Synth90k[53]

English

~9000000

√

Regular

SynthText[54]

English

~6000000

√

Regular

Verisimilar Synthesis[73]

English

~5000000

√

Regular

UnrealText[88]

English

~600000

~12000000

√

Regular

545 | 546 | *** 547 | 548 | 549 | ## 2. Performance Comparison of Recognition Algorithms 550 | 551 | 552 | ### 2.1 Characteristics Comparison of Recognition Approaches 553 | 554 | It is notable that 1) "Reg" stands for regular Latin datasets. 2) "Irreg" stands for irregular Latin datasets. 3) "Seg" denotes the segmentation-based methods. 4) "Extra" indicates the methods that use the extra datasets other than Synth90k and SynthText. 5) "CTC" represents the methods that apply the CTC-based algorithm to decode. 6) "Attn" represents the method that apply the attention mechanism to decode. 555 | 556 | You can also download the new [Excel](https://pan.baidu.com/s/1xitxu7R5hw27pVV7eJ1c7w) prepared by us. (Password: sj2t) 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 578 | 579 | 580 | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | 590 | 591 | 592 | 593 | 594 | 595 | 596 | 597 | 598 | 599 | 600 | 601 | 602 | 603 | 604 | 605 | 606 | 607 | 608 | 609 | 610 | 611 | 612 | 613 | 614 | 615 | 616 | 617 | 618 | 619 | 620 | 621 | 622 | 623 | 624 | 625 | 626 | 627 | 628 | 629 | 630 | 631 | 632 | 633 | 634 | 635 | 636 | 637 | 638 | 639 | 640 | 641 | 642 | 643 | 644 | 645 | 646 | 647 | 648 | 649 | 650 | 651 | 652 | 653 | 654 | 655 | 656 | 657 | 658 | 659 | 660 | 661 | 662 | 663 | 664 | 665 | 666 | 667 | 668 | 669 | 670 | 671 | 672 | 673 | 674 | 675 | 676 | 677 | 678 | 679 | 680 | 681 | 682 | 683 | 684 | 685 | 686 | 687 | 688 | 689 | 690 | 691 | 692 | 693 | 694 | 695 | 696 | 697 | 698 | 699 | 700 | 701 | 702 | 703 | 704 | 705 | 706 | 707 | 708 | 709 | 710 | 711 | 712 | 713 | 714 | 715 | 716 | 717 | 718 | 719 | 720 | 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Characteristics Comparison of Recognition Approaches
Method	Code	Regular	Irregular	Segmentation	Extra data	CTC	Attention	Source	Time	Highlight
Method	Code	Regular	Irregular	Segmentation	Extra data	CTC	Attention	Source	Time	Highlight
Wang et al. [1] : ABBYY	√	√	×	√	×	×	×	ICCV	2011	a state-of-the-art text detector + a leading commercial OCR engine
Wang et al. [1] : SYNTH+PLEX	√	√	×	×	×	×	×	ICCV	2011	the baseline of scene text recognition
Mishra et al. [2]	×	√	×	√	×	×	×	BMVC	2012	1) incorporating higher order statistical language models to recognize words in an unconstrained manner 2) introducing IIIT5K-word dataset
Wang et al. [3]	√	√	×	√	×	×	×	ICPR	2012	CNNs + Non-maximal suppression + beam search
Goel et al. [4] : wDTW	×	√	×	√	×	×	×	ICDAR	2013	recognizing text by matching the scene and synthetic image features with wDTW
Bissacco et al. [5] : PhotoOCR	×	√	×	√	×	×	×	ICCV	2013	applying a network with five hidden layers for character classification
Phan et al. [6]	×	×	√	√	×	×	×	ICCV	2013	1) MSER + SIFT descriptors + SVM 2) introducing the SVT-P datasets
Alsharif et al. [7] : HMM/Maxout	×	√	×	√	×	×	×	ICLR	2014	convolutional Maxout networks + Hybrid HMM
Almazan et al [8] : KCSR	√	√	×	×	×	×	×	TPAMI	2014	embedding word images and text strings in a common vectorial subspace and interpreting the task of recognition and retrieval as a nearest neighbor problem
Yao et al. [9] : Strokelets	×	√	×	√	×	×	×	CVPR	2014	proposing a novel multi-scale representation for scene text recognition: strokelets
R.-Serrano et al.[10] : Label embedding	×	√	×	×	×	×	×	IJCV	2015	embedding word labels and word images into a common Euclidean space and finding the cloest word label in this space
Jaderberg et al. [11]	√	√	×	√	×	×	×	ECCV	2014	1) enabling efficient feature sharing for text detection and classification 2) making technical changes over the traditional CNN architectures 3) proposing a method of automated data mining of Flickr.
Su and Lu [12]	×	√	×	×	×	√	×	ACCV	2014	HOG + BLSTM + CTC
Gordo[13] : Mid-features	×	√	×	√	×	×	×	CVPR	2015	proposing local mid-level features for building word image representations
Jaderberg et al. [14]	√	√	×	×	×	×	×	IJCV	2015	1) treating each word as a category and training very large convolutional neural networks to perform word recognition on the whole proposal region 2) generating 9 million images with equal numbers of word samples from a 90k word dictionary
Jaderberg et al. [15]	×	√	×	×	×	×	×	ICLR	2015	CNN + CRF
Shi, Bai, and Yao [16] : CRNN	√	√	×	×	×	√	×	TPAMI	2017	CNN + BLSTM + CTC
Shi et al. [17] : RARE	×	×	√	×	×	×	√	CVPR	2016	STN + CNN + attentional BLSTM
Lee and Osindero [18] : R2AM	×	√	×	×	×	×	√	CVPR	2016	presenting recursive recurrent neural networks with attention modeling
Liu et al. [19] : STAR-Net	×	×	√	×	×	√	×	BMVC	2016	STN + ResNet + BLSTM + CTC
Liu et al. [78]	×	√	×	√	√	×	×	ICPR	2016	integrating the CNN and WFST classification model
Mishra et al. [77]	×	√	×	√	√	×	×	CVIU	2016	character detection (HOG/CNN + SVM +Sliding window) + CRF, combining bottom-up cues from character detection and top-down cues from lexicon
Su and Lu [76]	×	√	×	×	√	√	×	PR	2017	HOG(different scale) + BLSTM + CTC (ensemble)
*Yang et al. [20]	×	×	√	×	√	×	√	IJCAI	2017	1) CNN + 2D attention-based RNN, applying an auxiliary dense character detection task that helps to learn text specific visual patterns 2) developing a large-scale synthetic dataset
Yin et al. [21]	×	√	×	×	×	√	×	ICCV	2017	CNN + CTC
Wang et al.[66] : GRCNN	√	√	×	×	×	√	×	NIPS	2017	Gated Recurrent Convulution Layer + BLSTM + CTC
*Cheng et al. [22] : FAN	×	√	×	×	√	×	√	ICCV	2017	1) proposing the concept of attention drift 2)introducing focusing network to focus deviated attention back on the target areas
Cheng et al. [23] : AON	×	×	√	×	×	×	√	CVPR	2018	1) extracting scene text features in four directions 2) CNN + Attentional BLSTM
Gao et al. [24]	×	√	×	×	×	√	√	NC	2019	attentional ResNet + CNN + CTC
Liu et al. [25] : Char-Net	×	×	√	√	×	×	√	AAAI	2018	CNN + STN (facilitating the rectification of individual characters) + LSTM
*Liu et al. [26] : SqueezedText	×	√	×	×	√	×	×	AAAI	2018	binary convolutional encoder-decoder network + Bi-RNN
Zhan et al.[73]	√	√	×	×	√	√	×	CVPR	2018	CRNN, achieving verisimilar scene text image synthesis by combining three novel designs, including semantic coherence, visual attention, and adaptive text appearance
*Bai et al. [27] : EP	×	√	×	×	√	×	√	CVPR	2018	proposing edit probability to effectively handle the misalignment between the training text and the output probability distribution sequence
Fang et al.[74]	√	√	×	×	×	×	√	MultiMedia	2018	ResNet + [2D Attentional CNN, CNN-based language module]
Liu et al.[75] : EnEsCTC	√	√	×	×	×	√	×	NIPS	2018	proposing a novel maximum entropy based regularization for CTC (EnCTC) and an entropy-based pruning method (EsCTC) to effectively reduce the space of the feasible set
Liu et al. [28]	×	√	×	×	×	√	×	ECCV	2018	designing a multi-task network with an encoder-discriminator-generator architecture to guide the feature of the original image toward that of the clean image
Wang et al.[61] : MAAN	×	√	×	×	×	×	√	ICFHR	2018	ResNet + BLSTM + Memory-Augmented attentional decoder
Gao et al. [29]	×	√	×	×	×	√	√	ICIP	2018	attentional DenseNet + BLSTM + CTC
Shi et al. [30] : ASTER	√	×	√	×	×	×	√	TPAMI	2018	TPS + ResNet + bidirectional attention-based BLSTM
Chen et al. [60] : ASTER + AEG	×	×	√	×	×	×	√	NC	2019	TPS + ResNet + bidirectional attention-based BLSTM + AEG
Luo et al. [46] : MORAN	√	×	√	×	×	×	√	PR	2019	Multi-object rectification network + CNN + attentional BLSTM
Luo et al. [61] : MORAN-v2	√	×	√	×	×	×	√	PR	2019	Multi-object rectification network + ResNet + attentional BLSTM
Chen et al. [60] : MORAN-v2 + AEG	×	×	√	×	×	×	√	NC	2019	Multi-object rectification network + ResNet + attentional BLSTM + AEG
Xie et al. [47] : CAN	×	√	×	×	×	×	√	ACM	2019	ResNet + CNN + GLU
*Liao et al.[48] : CA-FCN	×	×	√	√	√	×	√	AAAI	2019	performing character classification at each pixel location and needing character-level annotations
*Li et al. [49] : SAR	√	×	√	×	√	×	√	AAAI	2019	ResNet + 2D attentional LSTM
Zhan el at. [55]: ESIR	×	×	√	×	×	×	√	CVPR	2019	Iterative rectification Network + ResNet + attentional BLSTM
Zhang et al. [56]: SSDAN	×	√	×	√	×	×	√	CVPR	2019	attentional CNN + GAS + GRU
Yang et al. [62]: ScRN	×	×	√	×	√	×	√	ICCV	2019	Symmetry-constrained Rectification Network + ResNet + BLSTM + attentional GRU
Wang et al. [64]: GCAM	×	√	×	×	×	×	√	ICME	2019	Convolutional Block Attention Module (CBAM) + ResNet + BLSTM + the proposed Gated Cascade Attention Module (GCAM)
Jeonghun et al. [65]	√	×	√	×	×	×	√	ICCV	2019	TPS + ResNet + BLSTM + Attention Mechanism
Huang et al. [67] : EPAN	×	×	√	×	×	×	√	NC	2019	learning to sample features from the text region of 2D feature maps and innovatively introducing a two-stage attention mechanism
Gao et al. [68]	×	√	×	×	×	√	×	NC	2019	attentional DenseNET + 4-layer CNN + CTC
Qi et al. [69] : CCL	×	√	×	×	√	√	×	ICDAR	2019	ResNet + [CTC, CCL]
Wang et al. [70] : ReELFA	×	×	√	×	√	×	√	ICDAR	2019	VGG + attentional LSTM, utilizing one-hot encoded coordinates to indicate the spatial relationship of pixels and character center masks to help focus attention on the right feature areas
Zhu et al. [71] : HATN	×	×	√	×	√	×	√	ICIP	2019	ResNet50 + Hierarchical Attention Mechanism (Transformer structure)
Zhan et al. [72] : SF-GAN	×	√	×	×	√	×	√	CVPR	2019	ResNet50 + attentional Decoder, synthesising realistic scene text images for training better recognition models
Liao et al. [79] : SAM	√	×	√	×	×	×	√	TPAMI	2019	Spatial attentional module (SAM)
Liao et al. [79] : seg-SAM	√	×	√	×	√	×	√	TPAMI	2019	Character segmentation module + Spatial attention module (SAM)
Wang et al. [80] : DAN	√	×	√	×	×	×	√	AAAI	2020	decoupling the decoder of the traditional attention mechanism into a convolutional alignment module and a decoupled text decoder
Wang et al. [82] : TextSR	√	×	√	×	×	×	√	arXiv	2019	attempting to solve small texts with super-resolution methods
Wan et al. [83] : TextScanner	×	×	√	√	√	×	×	AAAI	2020	an effective segmentation-based dual-branch framework for scene text recognition
Hu et al. [84] : GTC	×	×	√	×	√	√	√	AAAI	2020	attempting to use GCN to learn the local correlations of feature sequence
Luo et al. [85]	×	×	√	×	×	×	√	IJCV	2020	separating text content from noisy background styles
*Litman et al. [86]	×	×	√	×	√	×	√	CVPR	2020	training a deep BiLSTM encoder, thus improving the encoding of contextual dependencies
Yu et al. [87]	×	×	√	×	×	×	√	CVPR	2020	introducing a global semantic reasoning module (GSRM) to capture global semantic context through multi-way parallel transmission
Qiao et al. [101] : SEED	√	×	√	×	×	×	√	CVPR	2020	proposing a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts
Bleeker et al. [93] : Bi-STET	√	√	×	×	×	×	√	ECAI	2020	a novel bidirectional STR method with a single decoder for bidirectional text decoding
*Bartz et al. [94] : KISS	√	×	√	×	√	×	√	arXiv	2020	a new model for STR that consists of two ResNet based feature extractors, a spatial transformer, and a transformer
Zhang et al. [95] : SPIN	×	×	√	×	×	×	√	arXiv	2020	a new learnable geometric-unrelated module which allows the color manipulation of source data within the network
Lin et al. [96] : FASDA	×	√	×	×	×	×	√	arXiv	2020	implementing sequence-level domain adaption for STR
Zhang et al. [98] : AutoSTR	√	×	√	×	×	×	√	ECCV	2020	searching data-dependent backbones
Mou et al. [99] : PlugNet	×	×	√	×	×	×	√	ECCV	2020	combining the pluggable super-resolution unit to solve the low-quality text recognition from the feature-level
*Yue et al. [100] : RobustScanner	×	×	√	×	√	×	√	ECCV	2020	mitigating the misrecognition problem of the encoderdecoder with attention framework on contextless text images

1546 | 1547 | 1548 | ### 2.2 Performance Comparison on Benchmark Datasets 1549 | 1550 | In this section, we compare the performance of the current advanced algorithms on benchmark datasets, including IIIT5K，SVT，IC03，IC13，SVT-P，CUTE80，IC15，COCO-Text, RCTW-17, MWTI, CTW，SCUT-CTW1500, LSVT, ArT and ReCTS-25k. 1551 | 1552 | It is notable that 1) The '*' indicates the methods that use the extra datasets other than Synth90k and SynthText. 2) The **bold** represents the best recognition results. 3) '^' denotes the best recognition results of using extra datasets. 4) '@' represents the methods under different evaluation that only uses 1811 test images. 5) 'SK', 'ST', 'ExPu', 'ExPr' and 'Un' indicates the methods that use Synth90K, SynthText, Extra Public Data, Extra Private Data and unknown data, respectively. 6) 'D_A' means data augmentation. 7) IC5-S contains only 1811 cropped text instances. 1553 | 1554 | 1555 | #### 2.2.1 Performance Comparison of Recognition Algorithms on Regular Latin Datasets 1556 | 1557 | 1558 | 1559 | 1560 | 1561 | 1562 | 1563 | 1564 | 1565 | 1566 | 1567 | 1568 | 1569 | 1570 | 1571 | 1572 | 1573 | 1574 | 1575 | 1576 | 1577 | 1578 | 1579 | 1580 | 1581 | 1582 | 1583 | 1584 | 1585 | 1586 | 1587 | 1588 | 1589 | 1590 | 1591 | 1592 | 1593 | 1594 | 1595 | 1596 | 1597 | 1598 | 1599 | 1600 | 1601 | 1602 | 1603 | 1604 | 1605 | 1606 | 1607 | 1608 | 1609 | 1610 | 1611 | 1612 | 1613 | 1614 | 1615 | 1616 | 1617 | 1618 | 1619 | 1620 | 1621 | 1622 | 1623 | 1624 | 1625 | 1626 | 1627 | 1628 | 1629 | 1630 | 1631 | 1632 | 1633 | 1634 | 1635 | 1636 | 1637 | 1638 | 1639 | 1640 | 1641 | 1642 | 1643 | 1644 | 1645 | 1646 | 1647 | 1648 | 1649 | 1650 | 1651 | 1652 | 1653 | 1654 | 1655 | 1656 | 1657 | 1658 | 1659 | 1660 | 1661 | 1662 | 1663 | 1664 | 1665 | 1666 | 1667 | 1668 | 1669 | 1670 | 1671 | 1672 | 1673 | 1674 | 1675 | 1676 | 1677 | 1678 | 1679 | 1680 | 1681 | 1682 | 1683 | 1684 | 1685 | 1686 | 1687 | 1688 | 1689 | 1690 | 1691 | 1692 | 1693 | 1694 | 1695 | 1696 | 1697 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| 2413 | 2414 | 2415 | 2416 | 2417 | 2418 | 2419 | 2420 | 2421 | 2422 | 2423 | 2424 | 2425 | 2426 | 2427 | 2428 | 2429 | 2430 | 2431 | 2432 | 2433 | 2434 | 2435 | 2436 | 2437 | 2438 | 2439 | 2440 | 2441 | 2442 | 2443 | 2444 | 2445 | 2446 | 2447 | 2448 | 2449 | 2450 | 2451 | 2452 | 2453 | 2454 | 2455 | 2456 | 2457 | 2458 | 2459 | 2460 | 2461 | 2462 | 2463 | 2464 | 2465 | 2466 | 2467 | 2468 | 2469 | 2470 | 2471 | 2472 | 2473 | 2474 | 2475 | 2476 | 2477 | 2478 | 2479 | 2480 | 2481 | 2482 | 2483 | 2484 | 2485 | 2486 | 2487 | 2488 | 2489 | 2490 | 2491 | 2492 | 2493 | 2494 | 2495 | 2496 | 2497 | 2498 | 2499 | 2500 | 2501 | 2502 | 2503 | 2504 | 2505 | 2506 | 2507 | 2508 | 2509 | 2510 | 2511 | 2512 | 2513 | 2514 | 2515 | 2516 | 2517 | 2518 | 2519 | 2520 | 2521 | 2522 | 2523 | 2524 | 2525 | 2526 | 2527 | 2528 | 2529 | 2530 | 2531 | 2532 | 2533 | 2534 | 2535 | 2536 | 2537 | 2538 | 2539 | 2540 | 2541 | 2542 | 2543 | 2544 | 2545 | 2546 | 2547 | 2548 | 2549 | 2550 | 2551 | 2552 | 2553 | 2554 | 2555 | 2556 | 2557 | 2558 | 2559 | 2560 | 2561 | 2562 | 2563 | 2564 | 2565 | 2566 | 2567 | 2568 | 2569 | 2570 | 2571 | 2572 | 2573 | 2574 | 2575 | 2576 | 2577 | 2578 | 2579 | 2580 | 2581 | 2582 | 2583 | 2584 | 2585 | 2586 | 2587 | 2588 | 2589 | 2590 | 2591 | 2592 | 2593 | 2594 | 2595 | 2596 | 2597 | 2598 | 2599 | 2600 | 2601 | 2602 | 2603 | 2604 | 2605 | 2606 | 2607 | 2608 | 2609 | 2610 | 2611 | 2612 | 2613 | 2614 | 2615 | 2616 | 2617 | 2618 | 2619 | 2620 | 2621 | 2622 | 2623 | 2624 | 2625 | 2626 | 2627 | 2628 | 2629 | 2630 | 2631 | 2632 | 2633 | 2634 | 2635 | 2636 | 2637 | 2638 | 2639 | 2640 | 2641 | 2642 | 2643 | 2644 | 2645 | 2646 | 2647 | 2648 | 2649 | 2650 | 2651 | 2652 | 2653 | 2654 | 2655 | 2656 | 2657 | 2658 | 2659 | 2660 | 2661 | 2662 | 2663 | 2664 | 2665 | 2666 | 2667 | 2668 | 2669 | 2670 | 2671 | 2672 | 2673 | 2674 | 2675 | 2676 | 2677 | 2678 | 2679 | 2680 | 2681 | 2682 | 2683 | 2684 | 2685 | 2686 | 2687 | 2688 | 2689 | 2690 | 2691 | 2692 | 2693 | 2694 | 2695 | 2696 | 2697 | 2698 | 2699 | 2700 | 2701 | 2702 | 2703 | 2704 | 2705 | 2706 | 2707 | 2708 | 2709 | 2710 | 2711 | 2712 | 2713 | 2714 | 2715 | 2716 | 2717 | 2718 | 2719 | 2720 | 2721 | 2722 | 2723 | 2724 | 2725 | 2726 | 2727 | 2728 | 2729 | 2730 | 2731 | 2732 | 2733 | 2734 | 2735 | 2736 | 2737 | 2738 | 2739 | 2740 | 2741 | 2742 | 2743 | 2744 | 2745 | 2746 | 2747 | 2748 | 2749 | 2750 | 2751 | 2752 | 2753 | 2754 | 2755 | 2756 | 2757 | 2758 | 2759 | 2760 | 2761 | 2762 | 2763 | 2764 | 2765 | 2766 | 2767 | 2768 | 2769 | 2770 | 2771 |

Performance Comparison of Recognition Algorithms on Regular Latin Datasets
Method	IIIT5K			SVT		IC03				IC13	Data	Source	Time
Method	50	1K	None	50	None	50	Full	50k	None	None	Data	Source	Time
Wang et al. [1] : ABBYY	24.3	-	-	35	-	56	55	-	-	-	Un	ICCV	2011
Wang et al. [1] : SYNTH+PLEX	-	-	-	57	-	76	62	-	-	-	ExPr	ICCV	2011
Mishra et al. [2]	64.1	57.5	-	73.2	-	81.8	67.8	-	-	-	ExPu	BMVC	2012
Wang et al. [3]	-	-	-	70	-	90	84	-	-	-	ExPr	ICPR	2012
Goel et al. [4] : wDTW	-	-	-	77.3	-	89.7	-	-	-	-	Un	ICDAR	2013
Bissacco et al. [5] : PhotoOCR	-	-	-	90.4	78	-	-	-	-	87.6	ExPr	ICCV	2013
Phan et al. [6]	-	-	-	73.7	-	82.2	-	-	-	-	ExPu	ICCV	2013
Alsharif et al. [7] : HMM/Maxout	-	-	-	74.3	-	93.1	88.6	85.1	-	-	ExPu	ICLR	2014
Almazan et al [8] : KCSR	88.6	75.6	-	87	-	-	-	-	-	-	ExPu	TPAMI	2014
Yao et al. [9] : Strokelets	80.2	69.3	-	75.9	-	88.5	80.3	-	-	-	ExPu	CVPR	2014
R.-Serrano et al.[10] : Label embedding	76.1	57.4	-	70	-	-	-	-	-	-	ExPu	IJCV	2015
Jaderberg et al. [11]	-	-	-	86.1	-	96.2	91.5	-	-	-	ExPu	ECCV	2014
Su and Lu [12]	-	-	-	83	-	92	82	-	-	-	ExPu	ACCV	2014
Gordo[13] : Mid-features	93.3	86.6	-	91.8	-	-	-	-	-	-	ExPu	CVPR	2015
Jaderberg et al. [14]	97.1	92.7	-	95.4	80.7	98.7	98.6	93.3	93.1	90.8	ExPr	IJCV	2015
Jaderberg et al. [15]	95.5	89.6	-	93.2	71.7	97.8	97	93.4	89.6	81.8	SK + ExPr	ICLR	2015
Shi, Bai, and Yao [16] : CRNN	97.8	95	81.2	97.5	82.7	98.7	98	95.7	91.9	89.6	SK	TPAMI	2017
Shi et al. [17] : RARE	96.2	93.8	81.9	95.5	81.9	98.3	96.2	94.8	90.1	88.6	SK	CVPR	2016
Lee and Osindero [18] : R2AM	96.8	94.4	78.4	96.3	80.7	97.9	97	-	88.7	90	SK	CVPR	2016
Liu et al. [19] : STAR-Net	97.7	94.5	83.3	95.5	83.6	96.9	95.3	-	89.9	89.1	SK + ExPr	BMVC	2016
*Liu et al. [78]	94.1	84.7	-	92.5	-	96.8	92.2	-	-	-	ExPu (D_A)	ICPR	2016
*Mishra et al. [77]	78.07	-	46.73	78.2	-	88	-	-	67.7	60.18	ExPu (D_A)	CVIU	2016
*Su and Lu [76]	-	-	-	91	-	95	89	-	-	76	SK + ExPu	PR	2017
*Yang et al. [20]	97.8	96.1	-	95.2	-	97.7	-	-	-	-	ExPu	IJCAI	2017
Yin et al. [21]	98.7	96.1	78.2	95.1	72.5	97.6	96.5	-	81.1	81.4	SK	ICCV	2017
Wang et al.[66] : GRCNN	98	95.6	80.8	96.3	81.5	98.8	97.8	-	91.2	-	SK	NIPS	2017
*Cheng et al. [22] : FAN	99.3	97.5	87.4	97.1	85.9	99.2	97.3	-	94.2	93.3	SK + ST (Pixel_wise)	ICCV	2017
Cheng et al. [23] : AON	99.6	98.1	87	96	82.8	98.5	97.1	-	91.5	-	SK + ST (D_A)	CVPR	2018
Gao et al. [24]	99.1	97.9	81.8	97.4	82.7	98.7	96.7	-	89.2	88	SK	NC	2019
Liu et al. [25] : Char-Net	-	-	83.6	-	84.4	-	93.3	-	91.5	90.8	SK (D_A)	AAAI	2018
*Liu et al. [26] : SqueezedText	97	94.1	87	95.2	-	98.8	97.9	93.8	93.1	92.9	ExPr	AAAI	2018
*Zhan et al.[73]	98.1	95.3	79.3	96.7	81.5	-	-	-	-	87.1	Pr(5 million)	CVPR	2018
*Bai et al. [27] : EP	99.5	97.9	88.3	96.6	87.5	98.7	97.9	-	94.6	94.4	SK + ST (Pixel_wise)	CVPR	2018
Fang et al.[74]	98.5	96.8	86.7	97.8	86.7	99.3	98.4	-	94.8	93.5	SK + ST	MultiMedia	2018
Liu et al.[75] : EnEsCTC	-	-	82	-	80.6	-	-	-	92	90.6	SK	NIPS	2018
Liu et al. [28]	97.3	96.1	89.4	96.8	87.1	98.1	97.5	-	94.7	94	SK	ECCV	2018
Wang et al.[61] : MAAN	98.3	96.4	84.1	96.4	83.5	97.4	96.4	-	92.2	91.1	SK	ICFHR	2018
Gao et al. [29]	99.1	97.2	83.6	97.7	83.9	98.6	96.6	-	91.4	89.5	SK	ICIP	2018
Shi et al. [30] : ASTER	99.6	98.8	93.4	97.4	89.5	98.8	98	-	94.5	91.8	SK + ST	TPAMI	2018
Chen et al. [60] : ASTER + AEG	99.5	98.5	94.4	97.4	90.3	99	98.3	-	95.2	95	SK + ST	NC	2019
Luo et al. [46] : MORAN	97.9	96.2	91.2	96.6	88.3	98.7	97.8	-	95	92.4	SK + ST	PR	2019
Luo et al. [61] : MORAN-v2	-	-	93.4	-	88.3	-	-	-	94.2	93.2	SK + ST	PR	2019
Chen et al. [60] : MORAN-v2 + AEG	99.5	98.7	94.6	97.4	90.4	98.8	98.3	-	95.3	95.3	SK + ST	NC	2019
Xie et al. [47] : CAN	97	94.2	80.5	96.9	83.4	98.4	97.8	-	91	90.5	SK	ACM	2019
*Liao et al.[48] : CA-FCN	^99.8	98.9	92	98.8	82.1	-	-	-	-	91.4	SK + ST+ ExPr	AAAI	2019
*Li et al. [49] : SAR	99.4	98.2	95	98.5	91.2	-	-	-	-	94	SK + ST + ExPr	AAAI	2019
Zhan el at. [55]: ESIR	99.6	98.8	93.3	97.4	90.2	-	-	-	-	91.3	SK + ST	CVPR	2019
Zhang et al. [56]: SSDAN	-	-	83.8	-	84.5	-	-	-	92.1	91.8	SK	CVPR	2019
*Yang et al. [62]: ScRN	99.5	98.8	94.4	97.2	88.9	99	98.3	-	95	93.9	SK + ST(char-level + word-level)	ICCV	2019
Wang et al. [64]: GCAM	-	-	93.9	-	91.3	-	-	-	95.3	95.7	SK + ST	ICME	2019
Jeonghun et al. [65]	-	-	87.9	-	87.5	-	-	-	94.4	92.3	SK + ST	ICCV	2019
Huang et al. [67]：EPAN	98.9	97.8	94	96.6	88.9	98.7	98	-	95	94.5	SK + ST	NC	2019
Gao et al. [68]	99.1	97.9	81.8	97.4	82.7	98.7	96.7	-	89.2	88	SK	NC	2019
*Qi et al. [69] : CCL	99.6	99.1	91.1	98	85.9	99.2	^98.8	-	93.5	92.8	SK + ST(char-level + word-level)	ICDAR	2019
*Wang et al. [70] : ReELFA	99.2	98.1	90.9	-	82.7	-	-	-	-	-	ST(char-level + word-level)	ICDAR	2019
*Zhu et al. [71] : HATN	-	-	88.6	-	82.2	-	-	-	91.3	91.1	SK(D_A) + Pu	ICIP	2019
*Zhan et al. [72] : SF-GAN	-	-	63	-	69.3	-	-	-	-	61.8	Pr(1 million)	CVPR	2019
Liao et al. [79] : SAM	99.4	98.6	93.9	98.6	90.6	98.8	98	-	95.2	95.3	SK + ST	TPAMI	2019
*Liao et al. [79] : seg-SAM	^99.8	^99.3	95.3	99.1	91.8	99	97.9	-	95	95.3	SK + ST (char-level)	TPAMI	2019
Wang et al. [80] : DAN	-	-	94.3	-	89.2	-	-	-	95	93.9	SK + ST	AAAI	2020
Wang et al. [82] : TextSR	-	-	92.5	98	87.2	-	-	-	93.2	91.3	SK + ST	arXiv	2019
*Wan et al. [83] : TextScanner	99.7	99.1	93.9	98.5	90.1	-	-	-	-	92.9	SK + ST (char-level)	AAAI	2020
*Hu et al. [84] : GTC	-	-	^95.8	-	^92.9	-	-	-	95.5	94.4	SK + ST + ExPu	AAAI	2020
Luo et al. [85]	99.6	98.8	95.6	99.4	92.9	99.1	98.8	-	96.2	96	SK + ST	IJCV	2020
*Litman et al. [86]	-	-	93.7	-	92.7	-	-	-	^96.3	93.9	SK + ST + ExPu	CVPR	2020
Yu et al. [87]	-	-	94.8	-	91.5	-	-	-	-	95.5	SK + ST	CVPR	2020
Qiao et al. [101] : SEED	-	-	93.8	-	89.6	-	-	-	-	92.8	SK + ST	CVPR	2020
Bleeker et al. [93] : Bi-STET	99.6	98.9	94.7	97.4	89	99.1	98.7	-	96	93.4	SK + ST	ECAI	2020
*Bartz et al. [94] : KISS	-	-	94.6	-	89.2	-	-	-	-	93.1	SK + ST + ExPu (D_A)	arXiv	2020
Zhang et al. [95] : SPIN	-	-	94.7	-	90.3	-	-	-	94.4	92.8	SK + ST	arXiv	2020
Lin et al. [96] : FASDA	-	-	-	96.5	88.3	99.1	97.5	-	94.8	94.4	SK	arXiv	2020
Zhang et al. [98] : AutoSTR	-	-	94.7	-	90.9	-	-	-	93.3	94.2	SK + ST	ECCV	2020
Mou et al. [99] : PlugNet	-	-	94.4	-	92.3	-	-	-	95.7	95	SK + ST	ECCV	2020
*Yue et al. [100] : RobustScanner	-	-	95.4	-	89.3	-	-	-	-	94.1	SK + ST + ExPu	ECCV	2020

2772 | 2773 | 2774 | #### 2.2.2 Performance Comparison of Recognition Algorithms on Irregular Latin Datasets 2775 | 2776 | 2777 | 2778 | 2779 | 2780 | 2781 | 2782 | 2783 | 2784 | 2785 | 2786 | 2787 | 2788 | 2789 | 2790 | 2791 | 2792 | 2793 | 2794 | 2795 | 2796 | 2797 | 2798 | 2799 | 2800 | 2801 | 2802 | 2803 | 2804 | 2805 | 2806 | 2807 | 2808 | 2809 | 2810 | 2811 | 2812 | 2813 | 2814 | 2815 | 2816 | 2817 | 2818 | 2819 | 2820 | 2821 | 2822 | 2823 | 2824 | 2825 | 2826 | 2827 | 2828 | 2829 | 2830 | 2831 | 2832 | 2833 | 2834 | 2835 | 2836 | 2837 | 2838 | 2839 | 2840 | 2841 | 2842 | 2843 | 2844 | 2845 | 2846 | 2847 | 2848 | 2849 | 2850 | 2851 | 2852 | 2853 | 2854 | 2855 | 2856 | 2857 | 2858 | 2859 | 2860 | 2861 | 2862 | 2863 | 2864 | 2865 | 2866 | 2867 | 2868 | 2869 | 2870 | 2871 | 2872 | 2873 | 2874 | 2875 | 2876 | 2877 | 2878 | 2879 | 2880 | 2881 | 2882 | 2883 | 2884 | 2885 | 2886 | 2887 | 2888 | 2889 | 2890 | 2891 | 2892 | 2893 | 2894 | 2895 | 2896 | 2897 | 2898 | 2899 | 2900 | 2901 | 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3045 | 3046 | 3047 | 3048 | 3049 | 3050 | 3051 | 3052 | 3053 | 3054 | 3055 | 3056 | 3057 | 3058 | 3059 | 3060 | 3061 | 3062 | 3063 | 3064 | 3065 | 3066 | 3067 | 3068 | 3069 | 3070 | 3071 | 3072 | 3073 | 3074 | 3075 | 3076 | 3077 | 3078 | 3079 | 3080 | 3081 | 3082 | 3083 | 3084 | 3085 | 3086 | 3087 | 3088 | 3089 | 3090 | 3091 | 3092 | 3093 | 3094 | 3095 | 3096 | 3097 | 3098 | 3099 | 3100 | 3101 | 3102 | 3103 | 3104 | 3105 | 3106 | 3107 | 3108 | 3109 | 3110 | 3111 | 3112 | 3113 | 3114 | 3115 | 3116 | 3117 | 3118 | 3119 | 3120 | 3121 | 3122 | 3123 | 3124 | 3125 | 3126 | 3127 | 3128 | 3129 | 3130 | 3131 | 3132 | 3133 | 3134 | 3135 | 3136 | 3137 | 3138 | 3139 | 3140 | 3141 | 3142 | 3143 | 3144 | 3145 | 3146 | 3147 | 3148 | 3149 | 3150 | 3151 | 3152 | 3153 | 3154 | 3155 | 3156 | 3157 | 3158 | 3159 | 3160 | 3161 | 3162 | 3163 | 3164 | 3165 | 3166 | 3167 | 3168 | 3169 | 3170 | 3171 | 3172 | 3173 | 3174 | 3175 | 3176 | 3177 | 3178 | 3179 | 3180 | 3181 | 3182 | 3183 | 3184 | 3185 | 3186 | 3187 | 3188 | 3189 | 3190 | 3191 | 3192 | 3193 | 3194 | 3195 | 3196 | 3197 | 3198 | 3199 | 3200 | 3201 | 3202 | 3203 | 3204 | 3205 | 3206 | 3207 | 3208 | 3209 | 3210 | 3211 | 3212 | 3213 | 3214 | 3215 | 3216 | 3217 | 3218 | 3219 | 3220 | 3221 | 3222 | 3223 | 3224 | 3225 | 3226 | 3227 | 3228 | 3229 | 3230 | 3231 | 3232 | 3233 | 3234 | 3235 | 3236 | 3237 | 3238 | 3239 | 3240 | 3241 | 3242 | 3243 | 3244 | 3245 | 3246 | 3247 | 3248 | 3249 | 3250 | 3251 | 3252 | 3253 | 3254 | 3255 | 3256 | 3257 | 3258 | 3259 | 3260 | 3261 | 3262 | 3263 | 3264 | 3265 | 3266 | 3267 | 3268 | 3269 | 3270 | 3271 | 3272 | 3273 | 3274 | 3275 | 3276 | 3277 | 3278 | 3279 | 3280 | 3281 | 3282 | 3283 | 3284 | 3285 | 3286 | 3287 | 3288 | 3289 | 3290 | 3291 | 3292 | 3293 | 3294 | 3295 | 3296 | 3297 | 3298 | 3299 | 3300 | 3301 | 3302 | 3303 | 3304 | 3305 | 3306 | 3307 | 3308 | 3309 | 3310 | 3311 | 3312 | 3313 | 3314 | 3315 | 3316 | 3317 | 3318 | 3319 | 3320 | 3321 | 3322 | 3323 | 3324 | 3325 | 3326 | 3327 | 3328 | 3329 | 3330 | 3331 | 3332 | 3333 | 3334 | 3335 | 3336 | 3337 | 3338 | 3339 | 3340 | 3341 | 3342 | 3343 | 3344 | 3345 | 3346 | 3347 | 3348 | 3349 | 3350 | 3351 | 3352 | 3353 | 3354 | 3355 | 3356 | 3357 | 3358 | 3359 | 3360 | 3361 | 3362 | 3363 | 3364 | 3365 | 3366 | 3367 | 3368 | 3369 | 3370 | 3371 | 3372 | 3373 | 3374 | 3375 | 3376 | 3377 | 3378 | 3379 | 3380 | 3381 | 3382 | 3383 | 3384 | 3385 | 3386 | 3387 | 3388 | 3389 | 3390 | 3391 | 3392 | 3393 | 3394 | 3395 | 3396 | 3397 | 3398 | 3399 | 3400 | 3401 | 3402 | 3403 | 3404 | 3405 | 3406 | 3407 | 3408 | 3409 | 3410 | 3411 | 3412 | 3413 | 3414 | 3415 | 3416 | 3417 | 3418 | 3419 | 3420 | 3421 | 3422 | 3423 | 3424 | 3425 | 3426 | 3427 | 3428 | 3429 | 3430 | 3431 | 3432 | 3433 | 3434 | 3435 | 3436 | 3437 | 3438 | 3439 | 3440 | 3441 | 3442 | 3443 | 3444 | 3445 | 3446 | 3447 | 3448 | 3449 | 3450 | 3451 | 3452 | 3453 | 3454 | 3455 | 3456 | 3457 | 3458 | 3459 | 3460 | 3461 | 3462 | 3463 | 3464 | 3465 | 3466 | 3467 | 3468 | 3469 | 3470 | 3471 | 3472 | 3473 | 3474 | 3475 | 3476 | 3477 | 3478 | 3479 | 3480 | 3481 | 3482 | 3483 | 3484 | 3485 | 3486 | 3487 | 3488 | 3489 | 3490 | 3491 | 3492 | 3493 | 3494 | 3495 | 3496 | 3497 | 3498 | 3499 | 3500 | 3501 | 3502 | 3503 | 3504 | 3505 | 3506 | 3507 | 3508 | 3509 | 3510 | 3511 | 3512 | 3513 | 3514 | 3515 | 3516 | 3517 | 3518 | 3519 | 3520 | 3521 | 3522 | 3523 | 3524 | 3525 | 3526 | 3527 | 3528 | 3529 | 3530 | 3531 | 3532 | 3533 | 3534 | 3535 | 3536 | 3537 | 3538 | 3539 | 3540 | 3541 | 3542 | 3543 | 3544 | 3545 | 3546 | 3547 | 3548 | 3549 | 3550 | 3551 | 3552 | 3553 | 3554 | 3555 | 3556 | 3557 | 3558 | 3559 | 3560 | 3561 | 3562 | 3563 | 3564 | 3565 | 3566 | 3567 | 3568 | 3569 | 3570 | 3571 | 3572 | 3573 | 3574 | 3575 | 3576 | 3577 | 3578 | 3579 | 3580 | 3581 | 3582 | 3583 | 3584 | 3585 | 3586 | 3587 | 3588 | 3589 | 3590 | 3591 | 3592 | 3593 | 3594 | 3595 | 3596 | 3597 | 3598 | 3599 | 3600 | 3601 | 3602 | 3603 | 3604 | 3605 | 3606 | 3607 | 3608 | 3609 | 3610 | 3611 | 3612 | 3613 | 3614 | 3615 | 3616 | 3617 | 3618 | 3619 | 3620 | 3621 | 3622 | 3623 | 3624 | 3625 | 3626 | 3627 | 3628 | 3629 | 3630 | 3631 | 3632 | 3633 | 3634 | 3635 | 3636 | 3637 | 3638 | 3639 | 3640 | 3641 | 3642 | 3643 | 3644 | 3645 | 3646 | 3647 | 3648 | 3649 | 3650 | 3651 | 3652 | 3653 | 3654 | 3655 | 3656 | 3657 | 3658 | 3659 | 3660 | 3661 | 3662 | 3663 | 3664 | 3665 | 3666 | 3667 | 3668 | 3669 | 3670 | 3671 | 3672 | 3673 | 3674 | 3675 | 3676 | 3677 | 3678 | 3679 | 3680 | 3681 | 3682 | 3683 | 3684 | 3685 | 3686 | 3687 | 3688 | 3689 | 3690 | 3691 | 3692 | 3693 | 3694 | 3695 | 3696 | 3697 | 3698 | 3699 | 3700 | 3701 | 3702 | 3703 | 3704 | 3705 | 3706 | 3707 | 3708 | 3709 | 3710 | 3711 | 3712 | 3713 | 3714 | 3715 | 3716 | 3717 | 3718 | 3719 | 3720 | 3721 | 3722 | 3723 | 3724 | 3725 | 3726 | 3727 | 3728 | 3729 | 3730 | 3731 | 3732 | 3733 | 3734 | 3735 | 3736 | 3737 | 3738 | 3739 | 3740 | 3741 | 3742 | 3743 | 3744 | 3745 | 3746 | 3747 | 3748 | 3749 | 3750 | 3751 | 3752 | 3753 | 3754 | 3755 | 3756 | 3757 | 3758 | 3759 | 3760 | 3761 | 3762 | 3763 | 3764 | 3765 | 3766 | 3767 |

Performance Comparison of Recognition Algorithms on Irregular Latin Datasets
Method	SVT-P			CUTE80	IC15-S	IC15	COCO-TEXT	Data	Source	Time
Method	50	Full	None	None	None	None	None	Data	Source	Time
Wang et al. [1] : ABBYY	40.5	26.1	-	-	-	-	-	Un	ICCV	2011
Wang et al. [1] : SYNTH+PLEX	-	-	-	-	-	-	-	ExPr	ICCV	2011
Mishra et al. [2]	45.7	24.7	-	-	-	-	-	ExPu	BMVC	2012
Wang et al. [3]	40.2	32.4	-	-	-	-	-	ExPr	ICPR	2012
Goel et al. [4] : wDTW	-	-	-	-	-	-	-	Un	ICDAR	2013
Bissacco et al. [5] : PhotoOCR	-	-	-	-	-	-	-	ExPr	ICCV	2013
Phan et al. [6]	62.3	42.2	-	-	-	-	-	ExPu	ICCV	2013
Alsharif et al. [7] : HMM/Maxout	-	-	-	-	-	-	-	ExPu	ICLR	2014
Almazan et al [8] : KCSR	-	-	-	-	-	-	-	ExPu	TPAMI	2014
Yao et al. [9] : Strokelets	-	-	-	-	-	-	-	ExPu	CVPR	2014
R.-Serrano et al.[10] : Label embedding	-	-	-	-	-	-	-	ExPu	IJCV	2015
Jaderberg et al. [11]	-	-	-	-	-	-	-	ExPu	ECCV	2014
Su and Lu [12]	-	-	-	-	-	-	-	ExPu	ACCV	2014
Gordo[13] : Mid-features	-	-	-	-	-	-	-	ExPu	CVPR	2015
Jaderberg et al. [14]	-	-	-	-	-	-	-	ExPr	IJCV	2015
Jaderberg et al. [15]	-	-	-	-	-	-	-	SK + ExPr	ICLR	2015
Shi, Bai, and Yao [16] : CRNN	-	-	-	-	-	-	-	SK	TPAMI	2017
Shi et al. [17] : RARE	91.2	77.4	71.8	59.2	-	-	-	SK	CVPR	2016
Lee and Osindero [18] : R2AM	-	-	-	-	-	-	-	SK	CVPR	2016
Liu et al. [19] : STAR-Net	94.3	83.6	73.5	-	-	-	-	SK + ExPr	BMVC	2016
*Liu et al. [78]	-	-	-	-	-	-	-	ExPu (D_A)	ICPR	2016
*Mishra et al. [77]	-	-	-	-	-	-	-	ExPu (D_A)	CVIU	2016
*Su and Lu [76]	-	-	-	-	-	-	-	SK + ExPu	PR	2017
*Yang et al. [20]	93	80.2	75.8	69.3	-	-	-	ExPu	IJCAI	2017
Yin et al. [21]	-	-	-	-	-	-	-	SK	ICCV	2017
Wang et al.[66] : GRCNN	-	-	-	-	-	-	-	SK	NIPS	2017
*Cheng et al. [22] : FAN	-	-	-	-	70.6	-	-	SK + ST (Pixel_wise)	ICCV	2017
Cheng et al. [23] : AON	94	83.7	73	76.8	-	68.2	-	SK + ST (D_A)	CVPR	2018
Gao et al. [24]	-	-	-	-	-	-	-	SK	NC	2019
Liu et al. [25] : Char-Net	-	-	73.5	-	-	60	-	SK (D_A)	AAAI	2018
*Liu et al. [26] : SqueezedText	-	-	-	-	-	-	-	ExPr	AAAI	2018
*Zhan et al.[73]	-	-	-	-	-	-	-	Pr(5 million)	CVPR	2018
*Bai et al. [27] : EP	-	-	-	-	73.9	-	-	SK + ST (Pixel_wise)	CVPR	2018
Fang et al.[74]	-	-	-	-	-	71.2	-	SK + ST	MultiMedia	2018
Liu et al.[75] : EnEsCTC	-	-	-	-	-	-	-	SK	NIPS	2018
Liu et al. [28]	-	-	73.9	62.5		-	-	SK	ECCV	2018
Wang et al.[61] : MAAN	-	-	-	-	-	-	-	SK	ICFHR	2018
Gao et al. [29]	-	-	-	-	-	-	-	SK	ICIP	2018
Shi et al. [30] : ASTER	-	-	78.5	79.5	76.1	-	-	SK + ST	TPAMI	2018
Chen et al. [60] : ASTER + AEG	94.4	89.5	82	80.9	-	76.7	-	SK + ST	NC	2019
Luo et al. [46] : MORAN	94.3	86.7	76.1	77.4	-	68.8	-	SK + ST	PR	2019
Luo et al. [61] : MORAN-v2	-	-	79.7	81.9	-	73.9	-	SK + ST	PR	2019
Chen et al. [60] : MORAN-v2 + AEG	94.7	89.6	82.8	81.3	-	77.4	-	SK + ST	NC	2019
Xie et al. [47] : CAN	-	-	-	-	-	-	-	SK	ACM	2019
*Liao et al.[48] : CA-FCN	-	-	-	78.1	-	-	-	SK + ST+ ExPr	AAAI	2019
*Li et al. [49] : SAR	^95.8	91.2	^86.4	89.6	-	78.8	^66.8	SK + ST + ExPr	AAAI	2019
Zhan el at. [55]: ESIR	-	-	79.6	83.3	-	76.9	-	SK + ST	CVPR	2019
Zhang et al. [56]: SSDAN	-	-	-	-	-	-	-	SK	CVPR	2019
*Yang et al. [62]: ScRN	-	-	80.8	87.5	-	78.7	-	SK + ST(char-level + word-level)	ICCV	2019
Wang et al. [64]: GCAM	-	-	85.7	83.3	83.5	-	-	SK + ST	ICME	2019
Jeonghun et al. [65]	-	-	79.2	74	-	71.8	-	SK + ST	ICCV	2019
Huang et al. [67]：EPAN	91.2	86.4	79.4	82.6	-	73.9	-	SK + ST	NC	2019
Gao et al. [68]	-	-	-	-	-	62.3	40	SK	NC	2019
*Qi et al. [69] : CCL	-	-	-	-	72.9	-	-	SK + ST(char-level + word-level)	ICDAR	2019
*Wang et al. [70] : ReELFA	-	-	-	82.3	-	68.5	-	ST(char-level + word-level)	ICDAR	2019
*Zhu et al. [71] : HATN	-	-	73.5	75.7	-	70.1	-	SK(D_A) + Pu	ICIP	2019
*Zhan et al. [72] : SF-GAN	-	-	48.6	40.6	-	39	-	Pr(1 million)	CVPR	2019
Liao et al. [79] : SAM	-	-	82.2	87.8	-	77.3	-	SK + ST	TPAMI	2019
*Liao et al. [79] : seg-SAM	-	-	83.6	88.5	-	78.2	-	SK + ST (char-level)	TPAMI	2019
Wang et al. [80] : DAN	-	-	80	84.4	-	74.5	-	SK + ST	AAAI	2020
Wang et al. [82] : TextSR	-	-	77.4	78.9	-	75.6	-	SK + ST	arXiv	2019
*Wan et al. [83] : TextScanner	-	-	84.3	83.3	-	79.4	-	SK + ST (char-level)	AAAI	2020
*Hu et al. [84] : GTC	-	-	85.7	92.2	-	79.5	-	SK + ST + ExPu	AAAI	2020
Luo et al. [85]	95.8	91.5	85.1	91.3	83.9	81.4	-	SK + ST	IJCV	2020
*Litman et al. [86]	-	-	^86.9	87.5	-	82.2	-	SK + ST + ExPu	CVPR	2020
Yu et al. [87]	-	-	85.1	87.8	82.7	-	-	SK + ST	CVPR	2020
Qiao et al. [101] : SEED	-	-	81.4	83.6	80	-	-	SK + ST	CVPR	2020
Bleeker et al. [93] : Bi-STET	-	-	80.6	82.5	75.7	-	-	SK + ST	ECAI	2020
*Bartz et al. [94] : KISS	-	-	83.1	89.6	80.3	74.2	-	SK + ST + ExPu (D_A)	arXiv	2020
Zhang et al. [95] : SPIN	-	-	82.8	87.5	82.2	78.5	-	SK + ST	arXiv	2020
Lin et al. [96] : FASDA	-	-	-	-	73.3	-	-	SK	arXiv	2020
Zhang et al. [98] : AutoSTR	-	-	81.7	-	81.8	-	-	SK + ST	ECCV	2020
Mou et al. [99] : PlugNet	-	-	84.3	85	-	82.2	-	SK + ST	ECCV	2020
*Yue et al. [100] : RobustScanner	-	-	82.9	^92.4	-	79.2	-	SK + ST + ExPu	ECCV	2020

3768 | 3769 | *** 3770 | 3771 | 3772 | ## 3. Survey 3773 | 3774 | **[50] \[TPAMI-2015]** Q. Ye and D. Doermann, “Text detection and recognition in imagery: A survey,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 37, no. 7, pp. 1480–1500, 2015. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6945320) 3775 | 3776 | **[51] \[Frontiers-Comput. Sci-2016]** Y. Zhu, C. Yao, and X. Bai, “Scene text detection and recognition: Recent advances and future trends,” Frontiers of Computer Science, vol. 10, no. 1, pp. 19–36, 2016. [paper](https://link.springer.com/article/10.1007/s11704-015-4488-0) 3777 | 3778 | **[52] \[IJCV-2020]** Long S, He X, Yao C. Scene text detection and recognition: The deep learning era[J]. International Journal of Computer Vision, 2020: 1-24. [paper](https://link.springer.com/article/10.1007/s11263-020-01369-0) [code](https://github.com/Jyouhou/SceneTextPapers) 3779 | 3780 | **[90]** **\[ACM Computing Surveys-2020]** X. Chen, L. Jin, Y. Zhu, C. Luo, and T. Wang, “Text Recognition in the Wild: A Survey," ACM Computing Surveys (CSUR) 2020. [paper](https://arxiv.org/pdf/2005.03492.pdf) [code](https://github.com/HCIILAB/Scene-Text-Recognition) 3781 | 3782 | *** 3783 | 3784 | 3785 | ## 4. OCR Service 3786 | 3787 | | OCR | API | Free | Code | 3788 | | :----------------------------------------------------------: | :--: | :--: | :--: | 3789 | | [Tesseract OCR Engine](https://github.com/tesseract-ocr/tesseract) | × | √ | √ | 3790 | | [Azure](https://azure.microsoft.com/zh-cn/services/cognitive-services/computer-vision/#Analysis) | √ | √ | × | 3791 | | [ABBYY](https://www.abbyy.cn/real-time-recognition-sdk/technical-specifications/) | √ | √ | × | 3792 | | [OCR Space](https://ocr.space/) | √ | √ | × | 3793 | | [SODA PDF OCR](https://www.sodapdf.com/ocr-pdf/) | √ | √ | × | 3794 | | [Free Online OCR](https://www.newocr.com/) | √ | √ | × | 3795 | | [Online OCR](https://www.onlineocr.net/) | √ | √ | × | 3796 | | [Super Tools](https://www.wdku.net/) | √ | √ | × | 3797 | | [Online Chinese Recognition](http://chongdata.com/ocr/) | √ | √ | × | 3798 | | [Calamari OCR](https://github.com/Calamari-OCR/calamari) | × | √ | √ | 3799 | | [ Tencent OCR](https://cloud.tencent.com/product/ocr?lang=cn) | √ | × | × | 3800 | 3801 | *** 3802 | 3803 | 3804 | ## 5. References 3805 | 3806 | **[1] \[ICCV-2011]** K. Wang, B. Babenko, and S. Belongie, “End-to-end scene text recognition,” in Proceedings of ICCV, 2011, pp. 1457–1464. [paper ](https://www.researchgate.net/profile/Serge_Belongie/publication/221110077_End-to-end_scene_text_recognition/links/09e4150b34908d2ebb000000/End-to-end-scene-text-recognition.pdf) 3807 | 3808 | **[2] \[BMVC-2012]** A. Mishra, K. Alahari, and C. Jawahar, “Scene text recognition using higher order language priors,” in Proceedings of BMVC, 2012, pp. 1–11. [paper](https://hal.inria.fr/hal-00818183/document) [dataset](http://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset) 3809 | 3810 | **[3] \[ICPR-2012]** T. Wang, D. J. Wu, A. Coates, and A. Y. Ng, “End-to-end text recognition with convolutional neural networks,” in Proceedings of ICPR, 2012, pp. 3304–3308. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460871&tag=1) 3811 | 3812 | **[4] \[ICDAR-2013]** V. Goel, A. Mishra, K. Alahari, and C. Jawahar, “Whole is greater than sum of parts: Recognizing scene text words,” in Proceedings of ICDAR, 2013, pp. 398–402. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6628652) 3813 | 3814 | **[5] \[ICCV-2013]** A. Bissacco, M. Cummins, Y. Netzer, and H. Neven, “Photoocr: Reading text in uncontrolled conditions,” in Proceedings of ICCV, 2013, pp. 785–792. [paper](http://openaccess.thecvf.com/content_iccv_2013/papers/Bissacco_PhotoOCR_Reading_Text_2013_ICCV_paper.pdf) 3815 | 3816 | **[6] \[ICCV-2013]** T. Quy Phan, P. Shivakumara, S. Tian, and C. Lim Tan, “Recognizing text with perspective distortion in natural scenes,” in Proceedings of ICCV, 2013, pp. 569–576. [paper](http://openaccess.thecvf.com/content_iccv_2013/papers/Phan_Recognizing_Text_with_2013_ICCV_paper.pdf) 3817 | 3818 | **[7] \[ICLR-2014]** O. Alsharif and J. Pineau, “End-to-end text recognition with hybrid HMM maxout models,” in Proceedings of ICLR: Workshop, 2014. [paper](https://arxiv.org/pdf/1310.1811.pdf) 3819 | 3820 | **[8] \[TPAMI-2014]** J. Almaz ́an, A. Gordo, A. Forn ́ es, and E. Valveny, "Word spotting and recognition with embedded attributes,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 36, no. 12, pp. 2552–2566, 2014. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6857995) [code](https://github.com/almazan/watts) 3821 | 3822 | **[9] \[CVPR-2014]** C. Yao, X. Bai, B. Shi, and W. Liu, “Strokelets: A learned multiscale representation for scene text recognition,” in Proceedings of CVPR, 2014, pp. 4042–4049. [paper](http://openaccess.thecvf.com/content_cvpr_2014/papers/Yao_Strokelets_A_Learned_2014_CVPR_paper.pdf) 3823 | 3824 | **[10] \[IJCV-2015]** J. A. Rodriguez-Serrano, A. Gordo, and F. Perronnin, “Label embedding: A frugal baseline for text recognition,” Int. J. Comput. Vis, vol. 113, no. 3, pp. 193–207, 2015. [paper](https://link.springer.com/content/pdf/10.1007%2Fs11263-014-0793-6.pdf) 3825 | 3826 | **[11] \[ECCV-2014]** M. Jaderberg, A. Vedaldi, and A. Zisserman, “Deep features for text spotting,” in Proceedings of ECCV, 2014, pp. 512–528. [paper](https://link.springer.com/content/pdf/10.1007%2F978-3-319-10593-2_34.pdf) [code](https://bitbucket.org/jaderberg/eccv2014_textspotting/src/master/) 3827 | 3828 | **[12] \[ACCV-2014]** B. Su and S. Lu, “Accurate scene text recognition based on recurrent neural network,” in Proceedings of ACCV, 2014, pp. 35–48. [paper](https://link.springer.com/content/pdf/10.1007%2F978-3-319-16865-4_3.pdf) 3829 | 3830 | **[13] \[CVPR-2015]** A. Gordo, “Supervised mid-level features for word image representation,” in Proceedings of CVPR, 2015, pp. 2956–2964. [paper](http://openaccess.thecvf.com/content_cvpr_2015/papers/Gordo_Supervised_Mid-Level_Features_2015_CVPR_paper.pdf) 3831 | 3832 | **[14] \[IJCV-2015]** M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman, “Reading text in the wild with convolutional neural networks,” Int. J. Comput. Vis, vol. 116, no. 1, pp. 1–20, 2016. [paper](https://link.springer.com/content/pdf/10.1007%2Fs11263-015-0823-z.pdf) [code](http://www.robots.ox.ac.uk/~vgg/research/text/) 3833 | 3834 | **[15] \[ICLR-2015]** M. Jaderberg, K. Simonyan, and A. Zisserman, “Deep structured output learning for unconstrained text recognition,” in Proceedings of ICLR, 2015. [paper](https://arxiv.org/pdf/1412.5903.pdf) 3835 | 3836 | **[16] \[TPAMI-2017]** B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 39, no. 11, pp. 2298–2304, 2017. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7801919) [code-Torch7](https://github.com/bgshih/crnn) [code-Pytorch](https://github.com/meijieru/crnn.pytorch) 3837 | 3838 | **[17] \[CVPR-2016]** B. Shi, X. Wang, P. Lyu, C. 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[paper](http://papers.nips.cc/paper/7363-connectionist-temporal-classification-with-maximum-entropy-regularization) [code](https://github.com/liuhu-bigeye/enctc.crnn ) 3961 | 3962 | **[76]** **\[PR-2017]** B. Su and S. Lu, “Accurate recognition of words in scenes without 3963 | character segmentation using recurrent neural network,” Pattern Recognition, vol. 63, pp. 397–405, 2017. [paper](https://www.sciencedirect.com/science/article/pii/S0031320316303314) 3964 | 3965 | **[77]** **\[CVIU-2016]** A. Mishra, K. Alahari, and C. Jawahar, “Enhancing energy minimization framework for scene text recognition with top-down cues,” Computer Vision and Image Understanding, vol. 145, pp. 30–42, 2016. [paper](https://www.sciencedirect.com/science/article/pii/S107731421600014X) 3966 | 3967 | **[78]** **\[ICPR-2016]** X. Liu, T. Kawanishi, X. Wu, and K. Kashino, “Scene text recognition with CNN classifier and WFST-based word labeling,” in Proceedings of ICPR, 2016, pp. 3999–4004. 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[paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690732.pdf) [code](https://github.com/AutoML-4Paradigm/AutoSTR.git) 4021 | 4022 | **[99]** **\[ECCV-2020]** Yan R, Huang Y, "PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit," in Proceedings of ECCV, 2020. [paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600154.pdf) 4023 | 4024 | **[100]** **\[ECCV-2020]** Yue X, Kuang Z, Lin C, et al. RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition," in Proceedings of ECCV, 2020. [paper](https://arxiv.org/pdf/2007.07542.pdf) 4025 | 4026 | **[101]** **\[CVPR-2020]** Zhi Qiao, Yu Zhou, Dongbao Yang, Yucan Zhou, and Weiping Wang. 2020. SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition. In Proceedings of CVPR. [paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qiao_SEED_Semantics_Enhanced_Encoder-Decoder_Framework_for_Scene_Text_Recognition_CVPR_2020_paper.pdf) [code]( https://github.com/Pay20Y/SEED) 4027 | 4028 | 4029 | 4030 | ## 6.Help 4031 | 4032 | If you have any problem in our resources, or any good paper/code we missed, please inform us at xxuechen@foxmail.com. Thank you for your contribution. 4033 | 4034 | *** 4035 | 4036 | 4037 | 4038 | 4039 | ## 7.Copyright 4040 | 4041 | Copyright © 2020 SCUT-DLVC. All Rights Reserved. 4042 | 4043 |

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