Pytesseract 或 Keras OCR 從圖像中提取文本 (Pytesseract or Keras OCR to extract text from image)

問題描述

Pytesseract 或 Keras OCR 從圖像中提取文本 (Pytesseract or Keras OCR to extract text from image)

我正在嘗試從圖像中提取文本。目前我得到空字符串作為輸出。下面是我的 pytesseract 代碼，儘管我也對 Keras OCR 持開放態度：‑

from PIL import Image
import pytesseract

path = 'captcha.svg.png'
img = Image.open(path)
captchaText = pytesseract.image_to_string(img, lang='eng', config='‑‑psm 6')

我不確定如何使用 svg 圖像，所以我將它們轉換為 png。下面是一些示例圖片：‑

keras‑ocr not working or returning nothing is because of the grayscale image (as I found it worked otherwise). See below:

from PIL import Image 

a = Image.open('/content/gD7vA.png') # return none by keras‑ocr, 
a.mode, a.split() # mode 1 channel + transparent layer / alpha layer (LA)

b = Image.open('/content/CYegU.png') # return result by keras‑ocr
b.mode, b.split() # mode RGB + transparent layer / alpha layer (RGBA)

In the above, the a is the file you mention in your question; as It showed, it has to channel, e.g. grayscale and transparent layer. And b is the file I converted to RGB or RGBA. The transparent layer already included in your original file and I didn't remove it, but it seems useless to keep otherwise if needed. In short, to make your input work on keras‑ocr, you can convert your files to RGB (or RGBA) first and save them on disk. And then pass them to ocr.

# Using PIL to convert one mode to another 
# and save on disk
c = Image.open('/content/gD7vA.png').convert('RGBA')
c.save(....png)
c.mode, c.split()

('RGBA',
 (<PIL.Image.Image image mode=L size=150x50 at 0x7F03E8E7A410>,
  <PIL.Image.Image image mode=L size=150x50 at 0x7F03E8E7A590>,
  <PIL.Image.Image image mode=L size=150x50 at 0x7F03E8E7A810>,
  <PIL.Image.Image image mode=L size=150x50 at 0x7F03E8E7A110>))

Full code

import matplotlib.pyplot as plt

# keras‑ocr will automatically download pretrained
# weights for the detector and recognizer.
pipeline = keras_ocr.pipeline.Pipeline()

# Get a set of three example images
images = [
         keras_ocr.tools.read(url) for url in [
            '/content/CYegU.png', # mode: RGBA; Only RGB should work too!
            '/content/bw6Eq.png', # mode: RGBA; 
            '/content/jH2QS.png', # mode: RGBA
            '/content/xbADG.png'  # mode: RGBA
    ]
]

# Each list of predictions in prediction_groups is a list of
# (word, box) tuples.
prediction_groups = pipeline.recognize(images)
Looking for /root/.keras‑ocr/craft_mlt_25k.h5
Looking for /root/.keras‑ocr/crnn_kurapan.h5

prediction_groups
[[('zum', array([[ 10.658852,  15.11916 ],
          [148.90204 ,  13.144257],
          [149.39563 ,  47.694347],
          [ 11.152428,  49.66925 ]], dtype=float32))],
 [('sresa', array([[  5.,  15.],
          [143.,  15.],
          [143.,  48.],
          [  5.,  48.]], dtype=float32))],
 [('sycw', array([[ 10.,  15.],
          [149.,  15.],
          [149.,  49.],
          [ 10.,  49.]], dtype=float32))],
 [('vdivize', array([[ 10.407883,  13.685192],
          [140.62648 ,  16.940662],
          [139.82323 ,  49.070583],
          [  9.604624,  45.815113]], dtype=float32))]]

Display

# Plot the predictions
fig, axs = plt.subplots(nrows=len(images), figsize=(20, 20))
for ax, image, predictions in zip(axs, images, prediction_groups):
    keras_ocr.tools.drawAnnotations(image=image, predictions=predictions, ax=ax)

(by Abhash Upadhyaya、M.Innat)

參考文件

1. Pytesseract or Keras OCR to extract text from image (CC BY‑SA 2.5/3.0/4.0)