一种基于CRNN的车牌识别算法研究与应用

Research and Application of a License Plate Recognition Algorithm Based on CRNN

近些年来,深度学习已逐渐替代机器学习而走进大众的生活当中,尤其是机器视觉,在拍照识图、光学字符识别(OCR)、自动驾驶等领域发挥着重要作用。文章针对智能交通中的车牌识别,提出一种基于CRNN的文本序列模型。该模型实现了端到端、免分割的车牌文字识别方法,充分体现了卷积神经网络的优势,在很多场景下都表现出较好的性能。经多次测试统计,该模型的识别准确率高达98.5%,平均响应时间为100 ms,测试效果良好。

In recent years,deep learning has gradually replaced machine learning and entered the public life,especially machine vision,which plays an important role in the fields of photo recognition,optical character recognition(OCR),automatic driving and so on.Aiming at the license plate recognition(LPR)in intelligent transportation,this paper proposes a text sequence model based on CRNN.The model realizes the end-to-end and segmentation free license plate character recognition method,fully reflects the advantages of convolutional neural network,and shows good performance in many scenes.After many tests and statistics,the recognition accuracy of the model is as high as 98.5%,the average response time is 100 ms,and the test effect is good.