如何用Python结合OpenCV提取低对比度细字体图片文本？

需求说明 #

我想从一张图片中提取文本数据，理想输出为：

42 Gandalf le Gris 2,247/2,300 2,035/2,200 1,068/1,100 274,232/285,800
35 Gorbag 7/100 355/1,250 37,530/207,500

输出顺序无关紧要。

尝试过的处理方法 #

我用Python的OpenCV库做了多种尝试，但都没得到理想结果，具体代码如下：

1. 提升图片对比度

lab= cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l_channel, a, b = cv2.split(lab)

clahe = cv2.createCLAHE(clipLimit=10.0, tileGridSize=(6,6))
cl = clahe.apply(l_channel)

limg = cv2.merge((cl,a,b))

image = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)

2. 遍历阈值做边缘检测

for a in range(1000):
    i +=3
    image = cv2.Canny(image_1, 100 + i, 100 + i)
    data = pytesseract.image_to_string(image, lang='eng', config='--psm 6')

3. 替换特定颜色为白色

for color in colors:
    rgb = color.split(',')
    image[np.all(image == (int(rgb[2]), int(rgb[1]), int(rgb[0])), axis=-1)] = (255, 255, 255)

4. 灰度转换+反转处理

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (1,1), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Morph open to remove noise and invert image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,1))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=0)
invert = 255 - opening

# Perform text extraction
image = pytesseract.image_to_string(image, lang='eng', config='--psm 6')
kernel = pytesseract.image_to_string(kernel, lang='eng', config='--psm 6')
invert = pytesseract.image_to_string(invert, lang='eng', config='--psm 6')

核心问题 #

• 数字的边缘线条过细
• 数字颜色与背景颜色过于接近

请问这种情况是否可以实现文本提取？

内容的提问来源于stack exchange，提问作者JackBow