基于LSTM 算法的车牌识别系统方法研究

本文针对自然光照以及阴雨天气等复杂环境导致识别精度、响应时间降低问题,提出一种将LSTM理论和算法融于复杂环境下的车牌识别算法。通过将采集的车牌图像进行腐蚀算法、灰度化、二值化等预处理,增强车牌区域对比度,减少定位难度;其次,利用图像识别处理算法进行车牌定位、字符分割、字符识别等操作。将车牌分割后所得的字符归一化处理,统一的合适大小,作为长短时记忆网络（LSTM）的输入,对应汉字字符、数字和字母字符作为输出,通过训练所得LSTM网络实现车牌识别系统模型。经大量数据采集以及训练验证,与现有车牌识别系统相比,本文提出的算法汉字字符识别准确率达98.90%,字母以及数字字符识别准确率达99.40%,单图识别速度达2.65ms。

Research on license plate recognition system based on LSTM algorithm

This paper proposes a license plate recognition algorithm based on LSTM theory and algorithm in complex environment, aiming at the problem that the recognition accuracy and response time are reduced due to natural light and rainy weather. In order to enhance the contrast of license plate area and reduce the difficulty of location, the license plate image is preprocessed with corrosion algorithm, gray scale and binarization. Secondly, image recognition processing algorithm is used for license plate location, character segmentation, character recognition and other operations. The characters obtained after the license plate segmentation are normalized and the appropriate size is unified as the input of the long and short duration memory network (LSTM), and the corresponding Chinese characters, numbers and alphabetic characters are as the output. The model of license plate recognition system is realized through the TRAINED LSTM network. After a lot of data collection and training, compared with the existing license plate recognition system, the algorithm proposed in this paper has a recognition accuracy of 98.90% Chinese characters, 99.40% alphanumeric characters and a single image recognition speed of 2.65ms.