从图像上删除边框，但保持文本写在边框上(在OCR之前进行预处理) [英] Remove borders from image but keep text written on borders (preprocessing before OCR)

问题描述

具有上面的图像，我可以将其裁剪为四个方框，使用OpenCV形态学操作(基本膨胀，腐蚀)去除边界，并得到如下结果:

Having an image such as one above, I am able to crop it into four square boxes, remove the borders using OpenCV morphological operations (basic dilation, erosion) and get a result such as:

在大多数情况下效果很好，但是如果有人在该行上书写，则可以预测为7而不是2.

Which works great in most cases, but if someone writes over the line, this may get predicted as 7 instead of 2.

我在寻找一种解决方案时遇到了麻烦，该解决方案可以在删除边框的同时恢复写在行上的字符部分.我拥有的图像已经转换为灰度，因此我无法根据颜色区分手写数字.解决这个问题的最佳方法是什么?

I am having trouble finding a solution that would recover the parts of the character written over the line while removing the borders. Images I have are already converted to grayscale so I can't distinguish written digits based on the color. What would be the best way to approach this problem?

推荐答案

这里是管道

• 将图像转换为灰度
• 大津获取二值图像的阈值
• 删除垂直线
• 删除水平线
• 构建修复内核并修复映像
• 反转图像

转换为灰度后，我们将Otsu的阈值

After converting to grayscale, we Otsu's threshold

从这里我们删除垂直线

然后删除水平线

这在字符上留下了空白，为了解决这个问题，我们创建了一个修复内核来扩大图像

This leaves us with a gap in the characters, to fix this, we create a repair kernel to dilate the image

接下来，我们按位排列-并使用阈值图像来保持角色细节

Next we bitwise-and with the thresholded image to maintain our character detail

差距仍然存在，但要好一些.我们执行变体"以缩小间隙

The gap is still there but a little better. We perform morph close to close the gap

现在已关闭，但我们丢失了角色详细信息.我们执行最终的按位运算，并使用阈值图像恢复细节

It's now closed but we lost character detail. We perform a final bitwise-and with the thresholded image to recover our detail

为了获得理想的结果，我们将图像反转

To get the desired result, we invert the image

import cv2

image = cv2.imread('1.png')
removed = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,40))
remove_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(remove_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(removed, [c], -1, (255,255,255), 15)

# Remove horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
remove_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(remove_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(removed, [c], -1, (255,255,255), 5)

# Repair kernel
repair_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
removed = 255 - removed
dilate = cv2.dilate(removed, repair_kernel, iterations=5)
dilate = cv2.cvtColor(dilate, cv2.COLOR_BGR2GRAY)
pre_result = cv2.bitwise_and(dilate, thresh)

result = cv2.morphologyEx(pre_result, cv2.MORPH_CLOSE, repair_kernel, iterations=5)
final = cv2.bitwise_and(result, thresh)

invert_final = 255 - final

cv2.imshow('thresh', thresh)
cv2.imshow('removed', removed)
cv2.imshow('dilate', dilate)
cv2.imshow('pre_result', pre_result)
cv2.imshow('result', result)
cv2.imshow('final', final)
cv2.imshow('invert_final', invert_final)
cv2.waitKey()

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