OCR的背景图像清洁 [英] Background image cleaning for OCR

问题描述

我试图通过tesseract-OCR从以下具有红色背景的图像中提取文本.

Through tesseract-OCR I am trying to extract text from the following images with a red background.

我在提取框B和D中的文本时遇到问题，因为有垂直线.我该如何清理背景:

I have problems extracting the text in boxes B and D because there are vertical lines. How can I clean the background like this:

输入:

输出:

有什么主意吗? 不带框的图像:

some idea? The image without boxes:

推荐答案

以下是使用Python OpenCV清理图像的两种方法

Here are two methods to clean the image using Python OpenCV

方法1:numpy阈值化

由于垂直线，水平线和背景为红色，因此我们可以利用这一点，并使用Numpy阈值将所有高于阈值的红色像素更改为白色.

Since the vertical lines, horizontal lines, and the background are in red we can take advantage of this and use Numpy thresholding to change all red pixels above a threshold to white.

import cv2
import numpy as np

image = cv2.imread('1.jpg')

image[np.where((image > [0,0,105]).all(axis=2))] = [255,255,255]

cv2.imshow('image', image)
cv2.waitKey()

方法2:传统图像处理

对于更通用的方法，如果线条不是红色，我们可以使用简单的图像处理技术来清洁图像.要删除垂直和水平线，我们可以构造特殊的内核以隔离线，并使用屏蔽和按位运算将其删除.删除线条后，我们可以使用阈值处理，形态学运算和轮廓过滤来删除红色背景.这是该过程的可视化

For a more general approach if the lines were not red we can use simple image processing techniques to clean the image. To remove the vertical and horizontal lines we can construct special kernels to isolate the lines and remove them using masking and bitwise operations. Once the lines are removed, we can use thresholding, morphological operations, and contour filtering to remove the red background. Here's a visualization of the process

首先我们构造垂直和水平内核，然后cv2.morphologyEx()来检测线.从这里开始，我们分别得到了水平线和垂直线的各个遮罩，然后按位排列-或两个遮罩获得了要删除所有线条的遮罩.接下来，我们按位-或与原始图像一起删除所有行

First we construct vertical and horizontal kernels then cv2.morphologyEx() to detect the lines. From here we have individual masks of the horizontal and vertical lines then bitwise-or the two masks to obtain a mask with all lines to remove. Next we bitwise-or with the original image to remove all lines

现在删除了线条，我们可以删除红色背景.我们阈值获取二进制图像并执行形态学操作以平滑文本

Now that the lines are removed, we can work on removing the red background. We threshold to obtain a binary image and perform morphological operations to smooth the text

仍然有一些小点，因此要去除它们，我们找到轮廓并使用最小阈值区域进行滤波以去除小噪声

There are still little dots so to remove them, we find contours and filter using a minimum threshold area to remove the small noise

最后我们反转图像以获得结果

Finally we invert the image to get our result

import cv2

image = cv2.imread('1.jpg')

# Remove vertical and horizontal lines
kernel_vertical = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
temp1 = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel_vertical)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,1))
temp2 = 255 - cv2.morphologyEx(image, cv2.MORPH_CLOSE, horizontal_kernel)
temp3 = cv2.add(temp1, temp2)
removed = cv2.add(temp3, image)

# Threshold and perform morphological operations
gray = cv2.cvtColor(removed, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY_INV)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)

# Filter using contour area and remove small noise
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 10:
        cv2.drawContours(close, [c], -1, (0,0,0), -1)

final = 255 - close 
cv2.imshow('removed', removed)
cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('final', final)
cv2.waitKey()

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