问题描述
拥有如上图所示的图像，我可以将其裁剪成四个方形框，使用 OpenCV 形态学操作(基本膨胀、腐蚀)去除边框并得到如下结果:
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:
这在大多数情况下效果很好，但如果有人越界写，这可能会被预测为 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.
我无法找到一种解决方案，该解决方案可以在删除边框的同时恢复写在线条上的字符部分.我拥有的图像已经转换为灰度，因此我无法根据颜色区分书写数字.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
这是一个管道
- 将图像转换为灰度
- Otsu 获取二值图像的阈值
- 去除竖线
- 去除水平线
- 构建修复内核和修复镜像
- 反转图像
转为灰度后，我们大津的阈值
After converting to grayscale, we Otsu's threshold
从这里我们删除垂直线
然后去掉水平线
这给我们留下了字符间隙，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
接下来我们使用阈值图像来保持我们的角色细节
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 morph close 以缩小差距
The gap is still there but a little better. We perform morph close to close the gap
它现在已经关闭，但我们丢失了角色细节.我们使用阈值图像执行最终的逐位与运算以恢复我们的细节
It's now closed but we lost character detail. We perform a final bitwise-and with the thresholded image to recover our detail
为了得到想要的结果，我们反转图像
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()
这篇关于从图像中删除边框，但将文本保留在边框上(OCR 之前的预处理)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！