python - 如何用Python提取识别车牌号？

Question

我尝试与 PIL 合作使用 pytesseract 从车牌图像中识别车辆登记号。但是我无法从这些图像中获取文本。

代码:

 from PIL import Image
 from pytesseract import image_to_string

 img= Image.open('D://carimage1')
 text = image_to_string(img)
 print(text)

虽然这适用于普通扫描文档，但不适用于车牌。

示例图片 1

示例图片 2

Answer 1

这里是关于如何解决您的问题的粗略想法。您可以在此基础上构建。您需要从图像中提取车牌，然后将图像发送到您的 tesseract。阅读代码注释以了解我正在尝试做什么。

import numpy as np
import cv2
import pytesseract
import matplotlib.pyplot as plt

img = cv2.imread('/home/muthu/Documents/3r9OQ.jpg')
#convert my image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#perform adaptive threshold so that I can extract proper contours from the image
#need this to extract the name plate from the image. 
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
contours,h = cv2.findContours(thresh,1,2)

#once I have the contours list, i need to find the contours which form rectangles.
#the contours can be approximated to minimum polygons, polygons of size 4 are probably rectangles
largest_rectangle = [0,0]
for cnt in contours:
    approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
    if len(approx)==4: #polygons with 4 points is what I need.
        area = cv2.contourArea(cnt)
        if area > largest_rectangle[0]:
            #find the polygon which has the largest size.
            largest_rectangle = [cv2.contourArea(cnt), cnt, approx]

x,y,w,h = cv2.boundingRect(largest_rectangle[1])
#crop the rectangle to get the number plate.
roi=img[y:y+h,x:x+w]
#cv2.drawContours(img,[largest_rectangle[1]],0,(0,0,255),-1)
plt.imshow(roi, cmap = 'gray')
plt.show()

输出的是下面附上的车牌:

现在将这个裁剪后的图像传递到您的 tesseract 中。

gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
text = pytesseract.image_to_string(roi)
print text

我得到了您分享的示例图像的以下输出。

如果将车牌图像透视变换为边界框矩形并移除周围多余的边框，解析会更加准确。如果您也需要这方面的帮助，请告诉我。

如果按原样使用，上面的代码不适用于第二张图片，因为我将搜索过滤为具有 4 个边的多边形。希望你明白了。