使用 OpenCV 和 Tesseract 的摩洛哥车牌识别 (LPR)

我正在开展一个关于识别摩洛哥车牌的项目，如下图所示：

摩洛哥车牌

请问我如何使用 OpenCV 切出车牌并使用 Tesseract 读取中间的数字和阿拉伯字母。

我研究过这篇研究论文：https://www.researchgate.net/publication/323808469_Moroccan_License_Plate_recognition_using_a_hybrid_method_and_license_plate_features 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 OpenCV 和用于 python 的 Tesseract。当我使用以下命令在车牌的仅文本部分上运行 tesseract 时"fra"我得到的语言7714315l Bv。如何分离数据？

Edit:我们在摩洛哥使用的阿拉伯字母是： � � � � � � � � � � � 预期结果是：77143 د 6垂直线是无关紧要的，我必须使用它们来分离图像并分别读取数据。

提前致谢！

您可以使用霍夫变换 https://docs.opencv.org/3.4.0/d9/db0/tutorial_hough_lines.html由于两条垂直线不相关，因此要裁剪图像：

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)

我相信你现在知道如何得到中间部分了;) 希望能帮助到你！

EDIT:

使用一些形态学操作，您还可以单独提取字符：

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)

Result: