├── README.md
├── data
    ├── test1.png
    ├── test2.png
    └── test3.png
├── main.py
└── requirements.txt


/README.md:
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1 | # text-detection-python-easyocr
2 | 
3 | In this video I show you how to make an optical character recognition algorithm using Python, OpenCV and EasyOCR in 15 minutes!
4 | 
5 | [![Watch the video](https://img.youtube.com/vi/n-8oCPjpEvM/0.jpg)](https://www.youtube.com/watch?v=n-8oCPjpEvM)
6 | 


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/data/test1.png:
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https://raw.githubusercontent.com/computervisioneng/text-detection-python-easyocr/f199a19f6285f77738d2b3fd12d4b956a2c7a949/data/test1.png


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/data/test2.png:
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https://raw.githubusercontent.com/computervisioneng/text-detection-python-easyocr/f199a19f6285f77738d2b3fd12d4b956a2c7a949/data/test2.png


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/data/test3.png:
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https://raw.githubusercontent.com/computervisioneng/text-detection-python-easyocr/f199a19f6285f77738d2b3fd12d4b956a2c7a949/data/test3.png


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/main.py:
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 1 | import cv2
 2 | import easyocr
 3 | import matplotlib.pyplot as plt
 4 | import numpy as np
 5 | 
 6 | # read image
 7 | image_path = '/home/phillip/Desktop/todays_tutorial/30_text_detection_easyocr/code/data/test2.png'
 8 | 
 9 | img = cv2.imread(image_path)
10 | 
11 | # instance text detector
12 | reader = easyocr.Reader(['en'], gpu=False)
13 | 
14 | # detect text on image
15 | text_ = reader.readtext(img)
16 | 
17 | threshold = 0.25
18 | # draw bbox and text
19 | for t_, t in enumerate(text_):
20 |     print(t)
21 | 
22 |     bbox, text, score = t
23 | 
24 |     if score > threshold:
25 |         cv2.rectangle(img, bbox[0], bbox[2], (0, 255, 0), 5)
26 |         cv2.putText(img, text, bbox[0], cv2.FONT_HERSHEY_COMPLEX, 0.65, (255, 0, 0), 2)
27 | 
28 | plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
29 | plt.show()
30 | 


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/requirements.txt:
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1 | easyocr==1.6.2
2 | matplotlib==3.7.1
3 | opencv-python-headless==4.5.4.60
4 | numpy==1.24.2
5 | 


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