使用OpenCV和Tesseract的摩洛哥车牌识别(LPR)

问题描述

我正在研究一个识别这张图的摩洛哥车牌的项目:

I'm working on a project about recognizing moroccan license plates which look like this image :

摩洛哥车牌

请问如何使用OpenCV切出车牌，如何使用Tesseract读取中间的数字和阿拉伯字母.

Please how can I use OpenCV to cut the license plate out and Tesseract to read the numbers and arabic letter in the middle.

我研究了这篇研究论文: https://www.research_hy_________

I have looked into this research paper : https://www.researchgate.net/publication/323808469_Moroccan_License_Plate_recognition_using_a_hybrid_method_and_license_plate_features

我已经在Windows 10中安装了适用于python的OpenCV和Tesseract.当我使用"fra"语言在车牌的仅文本部分运行tesseract时，我会得到7714315l Bv.如何分离数据?

I have installed OpenCV and Tesseract for python in Windows 10. When I run the tesseract on the text only part of the license plate using "fra" language I get 7714315l Bv. How can I separate the data?

我们在摩洛哥使用的阿拉伯字母是:أبتجحدهـ预期的结果是:77143 د 6垂直线无关紧要，我必须使用它们来分开图像并分别读取数据.

The arabic letters we use in Morocco are :أ ب ت ج ح د هـThe expected result is : 77143 د 6The vertical lines are irrelevant, I have to use them to separate the image and read data separately.

提前谢谢！

推荐答案

您可以使用 HoughTransform ，因为两条垂直线不相关，因此可以裁剪图像:

You can use HoughTransform since the two vertical lines are irrelevant, to crop the image:

import numpy as np
import cv2

image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

dst = cv2.Canny(grayImage, 0, 150)
cv2.imwrite("canny.jpg", dst)

lines = cv2.HoughLinesP(dst, 1, np.pi / 180, 50, None, 60, 20)

lines_x = []
# Get height and width to constrain detected lines
height, width, channels = image.shape
for i in range(0, len(lines)):
    l = lines[i][0]
    # Check if the lines are vertical or not
    angle = np.arctan2(l[3] - l[1], l[2] - l[0]) * 180.0 / np.pi
    if (l[2] > width / 4) and (l[0] > width / 4) and (70 < angle < 100):
        lines_x.append(l[2])
        # To draw the detected lines
        #cv2.line(image, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3, cv2.LINE_AA)

#cv2.imwrite("lines_found.jpg", image)
# Sorting to get the line with the maximum x-coordinate for proper cropping
lines_x.sort(reverse=True)
crop_image = "cropped_lines"
for i in range(0, len(lines_x)):
    if i == 0:
        # Cropping to the end
        img = image[0:height, lines_x[i]:width]
    else:
        # Cropping from the start
        img = image[0:height, 0:lines_x[i]]
    cv2.imwrite(crop_image + str(i) + ".jpg", img)

我确定您现在知道如何获得中间部分;)希望对您有帮助！

I am sure you know now how to get the middle part ;)Hope it helps!

编辑:

使用一些形态学操作，您还可以单独提取字符:

Using some morphological operations, you can also extract the characters individually:

import numpy as np
import cv2

image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

dst = cv2.Canny(grayImage, 50, 100)

dst = cv2.morphologyEx(dst, cv2.MORPH_RECT, np.zeros((5,5), np.uint8),
                       iterations=1)
cv2.imwrite("canny.jpg", dst)

im2, contours, heirarchy = cv2.findContours(dst, cv2.RETR_EXTERNAL,
                                            cv2.CHAIN_APPROX_NONE)

for i in range(0, len(contours)):
    if cv2.contourArea(contours[i]) > 200:
        x,y,w,h = cv2.boundingRect(contours[i])
        # The w constrain to remove the vertical lines
        if w > 10:
            cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 1)
            cv2.imwrite("contour.jpg", image)

结果:

这篇关于使用OpenCV和Tesseract的摩洛哥车牌识别(LPR)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！