1.读取图像
2.图像预处理(降噪)
3.定位到ROI(感兴趣区域)图像
4.对ROI图像进行车牌号文字识别提取

# 准备工作：导入相关库、定义需全局使用的函数、设置相关参数 import cv2 import imutils import numpy as np import pytesseract import re from matplotlib import pyplot as plt # %matplotlib inline def imshow(img): img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.imshow(img_rgb) 1.读取图像

img = cv2.imread('LicensePlate001.jpg',cv2.IMREAD_COLOR) img = cv2.resize(img, (600,400)) imshow(img)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 使用双边滤波(模糊)从图像中删除不需要的细节。 # gray = cv2.bilateralFilter(gray, 13, 15, 15) imshow(gray)

# 3-1.边缘检测 edged = cv2.Canny(gray, 30, 200) imshow(edged)

# 3-2.在图像上寻找轮廓，并定位ROI contours = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = imutils.grab_contours(contours) contours = sorted(contours, key = cv2.contourArea, reverse = True)[:10] ROI = None for c in contours: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.018 * peri, True) if len(approx) == 4: ROI = approx cv2.drawContours(img, [ROI], -1, (0, 0, 255), 2) break imshow(img)

# 3-3.从主图像裁剪出ROI（感兴趣区域）图像 y0,y1 = np.min(ROI[:,:,1]),np.max(ROI[:,:,1]) x0,x1 = np.min(ROI[:,:,0]),np.max(ROI[:,:,0]) Cropped = gray[y0:y1,x0:x1] imshow(Cropped)

text = pytesseract.image_to_string(Cropped) text = re.sub('[^a-zA-Z\d ]','',text) img2 = img.copy() cv2.putText(img2,text,(x0-50,y0),cv2.FONT_HERSHEY_SIMPLEX,1.2, (255, 255, 255), 2) imshow(img2)

附1：Tesseract文字识别可参考https://www.jianshu.com/p/24352fead613

[1].代码截止2020-08-29调试无误。
[2].如需全部代码及相关文件，留言邮箱。
[3].过程中有任何问题，欢迎交流！Q597966823