Python + OpenCV：OCR图像分割 [英] Python + OpenCV: OCR Image Segmentation

问题描述

我正试图从这个收据的玩具示例中做OCR。使用Python 2.7和OpenCV 3.1。

有任何建议吗？

以下代码是边缘检测前的示例，但是当收据与第一张图像相似时。我的问题不是图像文字。是图像的预处理。

任何帮助都超过赞赏！ ：）

  import os; 
 os.chdir（）＃放置你自己的目录
 
 import cv2 
 import numpy as np 
 
 image = cv2.imread（Rent-Receipt .jpg，cv2.IMREAD_GRAYSCALE）
 
 blurred = cv2.GaussianBlur（图片，（5,5），0）
 
 #blurred = cv2.bilateralFilter（灰色，9 ，75,75）
 
＃apply Canny边缘检测
 edged = cv2.Canny（模糊，0,20）
 
 #Find外部轮廓
 
（_，contours，_）= cv2.findContours（edged，cv2.RETR_EXTERNAL，cv2.CHAIN_APPROX_NONE）
  

解决方案

有关您所描述的第一步的精彩教程，请访问

现在，为了消除误报，你可以简单地循环通过船体中的点，并计算周长（船体中所有相邻点之间的距离之和[i]，其中船体[ i]是一个convexHull中所有点的列表。如果周长太大，则将其归类为不是字符。

图像中的诊断线即将到来，因为图像的边框是黑色的。只要读取图像就可以通过添加以下行来删除（第7行下面）

  img = img [ 5：-5,5：-5，：] 
  

给出输出

I am trying to do OCR from this toy example of Receipts. Using Python 2.7 and OpenCV 3.1.

Grayscale + Blur + External Edge Detection + Segmentation of each area in the Receipts (for example "Category" to see later which one is marked -in this case cash-).

I find complicated when the image is "skewed" to be able to properly transform and then "automatically" segment each segment of the receipts.

Example:

Any suggestion?

The code below is an example to get until the edge detection, but when the receipt is like the first image. My issue is not the Image to text. Is the pre-processing of the image.

Any help more than appreciated! :)

import os;
os.chdir() # Put your own directory

import cv2 
import numpy as np

image = cv2.imread("Rent-Receipt.jpg", cv2.IMREAD_GRAYSCALE)

blurred = cv2.GaussianBlur(image, (5, 5), 0)

#blurred  = cv2.bilateralFilter(gray,9,75,75)

# apply Canny Edge Detection
edged = cv2.Canny(blurred, 0, 20)

#Find external contour

(_,contours, _) = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

解决方案

A great tutorial on the first step you described is available at pyimagesearch (and they have great tutorials in general)

In short, as described by Ella, you would have to use cv2.CHAIN_APPROX_SIMPLE. A slightly more robust method would be to use cv2.RETR_LIST instead of cv2.RETR_EXTERNAL and then sort the areas, as it should decently work even in white backgrounds/if the page inscribes a bigger shape in the background, etc.

Coming to the second part of your question, a good way to segment the characters would be to use the Maximally stable extremal region extractor available in OpenCV. A complete implementation in CPP is available here in a project I was helping out in recently. The Python implementation would go along the lines of (Code below works for OpenCV 3.0+. For the OpenCV 2.x syntax, check it up online)

import cv2

img = cv2.imread('test.jpg')
mser = cv2.MSER_create()

#Resize the image so that MSER can work better
img = cv2.resize(img, (img.shape[1]*2, img.shape[0]*2))

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
vis = img.copy()

regions = mser.detectRegions(gray)
hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions[0]]
cv2.polylines(vis, hulls, 1, (0,255,0)) 

cv2.namedWindow('img', 0)
cv2.imshow('img', vis)
while(cv2.waitKey()!=ord('q')):
    continue
cv2.destroyAllWindows()

This gives the output as

Now, to eliminate the false positives, you can simply cycle through the points in hulls, and calculate the perimeter (sum of distance between all adjacent points in hulls[i], where hulls[i] is a list of all points in one convexHull). If the perimeter is too large, classify it as not a character.

The diagnol lines across the image are coming because the border of the image is black. that can simply be removed by adding the following line as soon as the image is read (below line 7)

img = img[5:-5,5:-5,:]

which gives the output

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