All Projects → duanshengliu → End To End For Chinese Plate Recognition

duanshengliu / End To End For Chinese Plate Recognition

基于u-net,cv2以及cnn的中文车牌定位,矫正和端到端识别软件,其中unet和cv2用于车牌定位和矫正,cnn进行车牌识别,unet和cnn都是基于tensorflow的keras实现

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End-to-end-for-chinese-plate-recognition

基于u-net,cv2以及cnn的中文车牌定位,矫正和端到端识别软件,其中unet和cv2用于车牌定位和矫正,cnn进行车牌识别,unet和cnn都是基于tensorflow的keras实现

环境:python:3.6, tensorflow:1.15.2, opencv: 4.1.0.25, keras: 2.3.1

整体思路:1. 利用u-net图像分割得到二值化图像,2. 再使用cv2进行边缘检测获得车牌区域坐标,并将车牌图形矫正,3. 利用卷积神经网络cnn进行车牌多标签端到端识别,具体描述可见CSDN博客:https://blog.csdn.net/qq_32194791/article/details/106748685

实现效果:拍摄角度倾斜、强曝光或昏暗环境等都能较好地识别,甚至有些百度AI车牌识别未能识别的图片也能识别

注意:若是直接识别类似下图的无需定位的完整车牌,那么请确保图片尺寸小于等于240 * 80,否则会被认为图片中含其余区域而进行定位,反而识别效果不佳

其余的没什么问题,正常识别都可以

部分效果图:

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