改善存在覆盖线的文本识别

问题描述 投票：0回答：2

这是一个简单的Python 3光学字符识别（OCR）程序来获取字符串，我已经在这里上传了目标gif文件，请下载并另存为

/tmp/target.gif
。

try:
    from PIL import Image
except ImportError:
    import Image
import pytesseract
print(pytesseract.image_to_string(Image.open('/tmp/target.gif')))

我将所有错误信息粘贴到此处，请修复它以从图像中获取字符。

/usr/lib/python3/dist-packages/PIL/Image.py:925: UserWarning: Couldn't allocate palette entry for transparency
  "for transparency")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.5/dist-packages/pytesseract/pytesseract.py", line 309, in image_to_string
    }[output_type]()
  File "/usr/local/lib/python3.5/dist-packages/pytesseract/pytesseract.py", line 308, in <lambda>
    Output.STRING: lambda: run_and_get_output(*args),
  File "/usr/local/lib/python3.5/dist-packages/pytesseract/pytesseract.py", line 208, in run_and_get_output
    temp_name, input_filename = save_image(image)
  File "/usr/local/lib/python3.5/dist-packages/pytesseract/pytesseract.py", line 136, in save_image
    image.save(input_file_name, format=img_extension, **image.info)
  File "/usr/lib/python3/dist-packages/PIL/Image.py", line 1728, in save
    save_handler(self, fp, filename)
  File "/usr/lib/python3/dist-packages/PIL/GifImagePlugin.py", line 407, in _save
    _get_local_header(fp, im, (0, 0), flags)
  File "/usr/lib/python3/dist-packages/PIL/GifImagePlugin.py", line 441, in _get_local_header
    transparency = int(transparency)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'tuple'

我用 bash 中的 

convert
命令转换它。

convert  "/tmp/target.gif"   "/tmp/target.jpg"

我在这里展示

/tmp/target.gif
和
/tmp/target.jpg
。

然后再次执行上面的python代码。

try:
    from PIL import Image
except ImportError:
    import Image
import pytesseract
print(pytesseract.image_to_string(Image.open('/tmp/target.jpg')))

我无法用

pytesseract.image_to_string(Image.open('/tmp/target.jpg'))
得到任何东西，我得到空白字符。

对于 Trenton_M 的代码：

>>> img1 = remove_noise_and_smooth(r'/tmp/target.jpg')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in remove_noise_and_smooth
AttributeError: 'NoneType' object has no attribute 'astype'
Thalish Sajeed

Thalish Sajeed 的代码：

省略

print(pytesseract.image_to_string(Image.open(filename)))
引起的错误信息。 

Type "help", "copyright", "credits" or "license" for more information.
>>> from PIL import Image
>>> import pytesseract
>>> import matplotlib.pyplot as plt
>>> import cv2
>>> import numpy as np
>>> 
>>> 
>>> def display_image(filename, length_box=60, width_box=30):
...     if type(filename) == np.ndarray:
...         image = filename
...     else:
...         image = cv2.imread(filename)
...     plt.figure(figsize=(length_box, width_box))
...     plt.imshow(image, cmap="gray")
... 
>>> 
>>> filename = r"/tmp/target.jpg"
>>> display_image(filename)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 7, in display_image
  File "/usr/local/lib/python3.5/dist-packages/matplotlib/pyplot.py", line 2699, in imshow
    None else {}), **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/matplotlib/__init__.py", line 1810, in inner
    return func(ax, *args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_axes.py", line 5494, in imshow
    im.set_data(X)
  File "/usr/local/lib/python3.5/dist-packages/matplotlib/image.py", line 634, in set_data
    raise TypeError("Image data cannot be converted to float")
TypeError: Image data cannot be converted to float
>>>

@Thalish Sajeed，为什么我用你的代码得到的是 

9244K
而不是 
0244k
？ 这是我测试过的示例文件。

提取的字符串。

@Trenton_M，纠正代码中的一些拼写错误和丢失，并根据您的建议删除行

plt.show()
。

>>> import cv2,pytesseract
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> 
>>> 
>>> def image_smoothening(img):
...     ret1, th1 = cv2.threshold(img, 88, 255, cv2.THRESH_BINARY)
...     ret2, th2 = cv2.threshold(th1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
...     blur = cv2.GaussianBlur(th2, (5, 5), 0)
...     ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
...     return th3
... 
>>> 
>>> def remove_noise_and_smooth(file_name):
...     img = cv2.imread(file_name, 0)
...     filtered = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 41)
...     kernel = np.ones((1, 1), np.uint8)
...     opening = cv2.morphologyEx(filtered, cv2.MORPH_OPEN, kernel)
...     closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
...     img = image_smoothening(img)
...     or_image = cv2.bitwise_or(img, closing)
...     return or_image
... 
>>> 
>>> cv2_thresh_list = [cv2.THRESH_BINARY, cv2.THRESH_TRUNC, cv2.THRESH_TOZERO]
>>> fn = r'/tmp/target.jpg'
>>> img1 = remove_noise_and_smooth(fn)
>>> img2 = cv2.imread(fn, 0)
>>> for i, img in enumerate([img1, img2]):
...     img_type = {0: 'Preprocessed Images\n',
...                 1: '\nUnprocessed Images\n'}
...     print(img_type[i])
...     for item in cv2_thresh_list:
...         print('Thresh: {}'.format(str(item)))
...         _, thresh = cv2.threshold(img, 127, 255, item)
...         plt.imshow(thresh, 'gray')
...         f_name = '{0}.jpg'.format(str(item))
...         plt.savefig(f_name)
...         print('OCR Result: {}\n'.format(pytesseract.image_to_string(f_name)))

... 预处理图像

在我的控制台中，所有输出信息如下：

Thresh: 0
<matplotlib.image.AxesImage object at 0x7fbc2519a6d8>
OCR Result: 10
15
20 

Edfifi
10
2 o 30 40 so
so

Thresh: 2
<matplotlib.image.AxesImage object at 0x7fbc255e7eb8>
OCR Result: 10
15
20
Edfifi
10
2 o 30 40 so
so
Thresh: 3
<matplotlib.image.AxesImage object at 0x7fbc25452fd0>
OCR Result: 10
15
20
Edfifi
10
2 o 30 40 so
so
Unprocessed Images
Thresh: 0
<matplotlib.image.AxesImage object at 0x7fbc25464c88>
OCR Result: 10
15
20
Thresh: 2
<matplotlib.image.AxesImage object at 0x7fbc254520f0>
OCR Result: 10
15
2o
2o
30 40 50
Thresh: 3
<matplotlib.image.AxesImage object at 0x7fbc1e1968d0>
OCR Result: 10
15
20

绳子

0244R
在哪里？

python python-3.x opencv ocr python-tesseract
2个回答
5
投票

让我们从 JPG 图像开始，因为 pytesseract 在 GIF 图像格式上运行存在问题。 参考

filename = "/tmp/target.jpg"
image = cv2.imread(filename)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, threshold = cv2.threshold(gray,55, 255, cv2.THRESH_BINARY)
print(pytesseract.image_to_string(threshold))

让我们尝试分解这里的问题。

您的图像噪声太大，超正方体引擎无法识别字母，我们使用一些简单的图像处理技术（例如灰度和阈值处理）来消除图像中的一些噪声。

然后，当我们将其发送到 OCR 引擎时，我们会看到字母的捕获更加准确。

如果您按照此github链接

，您可以找到我测试过的笔记本

编辑- 我已经用一些额外的图像清理技术更新了笔记本。 源图像的噪声太大，超正方体无法直接在图像上开箱即用。您需要使用图像清理技术。

您可以改变阈值参数或将高斯模糊替换为其他技术，直到获得所需的结果。

如果您希望在嘈杂的图像上运行 OCR - 请查看商业 OCR 提供商，例如 google-cloud-vision。他们每月免费提供 1000 次 OCR 调用。

2
投票

首先：确保您已经安装了Tesseract程序（不仅仅是python包）

解决方案的Jupyter Notebook：只有通过

remove_noise_and_smooth
的图像才能被OCR成功翻译。

尝试转换 image.gif 时，会生成 

TypeError: int() argument must be a string, a bytes-like object or a number, not 'tuple'
。

将 image.gif 重命名为 image.jpg，生成

TypeError

打开image.gif并“另存为”image.jpg，输出为空白，这意味着文本未被识别。

from PIL import Image
import pytesseract

# If you don't have tesseract executable in your PATH, include the following:
# your path may be different than mine
pytesseract.pytesseract.tesseract_cmd = "C:/Program Files (x86)/Tesseract-OCR/tesseract.exe"

imgo = Image.open('0244R_clean.jpg')

print(pytesseract.image_to_string(imgo))
  • 原始图像中无法识别任何文本，因此在 OCR 之前可能需要进行后处理以进行清理
  • 我创建了一个干净的图像，pytesseract 可以毫无问题地从中提取文本。该图像包含在下面，因此您可以使用自己的代码对其进行测试以验证功能。

添加后处理

使用图像预处理提高 OCR 的准确性

OpenCV

import cv2
import numpy as np
import matplotlib.pyplot as plt


def image_smoothening(img):
    ret1, th1 = cv2.threshold(img, 88, 255, cv2.THRESH_BINARY)
    ret2, th2 = cv2.threshold(th1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    blur = cv2.GaussianBlur(th2, (5, 5), 0)
    ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return th3


def remove_noise_and_smooth(file_name):
    img = cv2.imread(file_name, 0)
    filtered = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 41)
    kernel = np.ones((1, 1), np.uint8)
    opening = cv2.morphologyEx(filtered, cv2.MORPH_OPEN, kernel)
    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
    img = image_smoothening(img)
    or_image = cv2.bitwise_or(img, closing)
    return or_image


cv2_thresh_list = [cv2.THRESH_BINARY, cv2.THRESH_TRUNC, cv2.THRESH_TOZERO]

fn = r'/tmp/target.jpg'
img1 = remove_noise_and_smooth(fn)
img2 = cv2.imread(fn, 0)
for i, img in enumerate([img1, img2]):
    img_type = {0: 'Preprocessed Images\n',
                1: '\nUnprocessed Images\n'}
    print(img_type[i])
    for item in cv2_thresh_list:
        print('Thresh: {}'.format(str(item)))
        _, thresh = cv2.threshold(img, 127, 255, item)
        plt.imshow(thresh, 'gray')
        f_name = '{}_{}.jpg'.format(i, str(item))
        plt.savefig(f_name)
        print('OCR Result: {}\n'.format(pytesseract.image_to_string(f_name)))

img1 将生成以下新图像：

img2 将生成这些新图像：

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