Iranian License Plate Recognition using Deep Learning

2020 International Conference on Machine Vision and Image Processing (MVIP)(2020)

引用 4|浏览1
暂无评分
摘要
Automated License Plate Recognition (ALPR) has many applications in intelligent transport system. The ALPR has three main steps, License Plate (LP) localization, segmentation and Optical Character Recognition (OCR). Each step needs different techniques in real condition and each technique has its specific characteristics. The LP localization techniques detect the LP after that segmentation algorithms should segment and isolate each character from each other. Finally, the OCR step is applied to recognize the separated characters. The final accuracy depends on the accuracy of each step. To improve the OCR step performance, we combine both segmentation and OCR steps as a single-stage using deep learning techniques such as the You Only Look Once (YOLO) framework. Our experimental results show that this proposed approach recognizes the Iranian LP characters with accuracy 99.2% compared to previous works.
更多
查看译文
关键词
optical character recognition,deep learning,YOLO,artificial neural network,support vector machine
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要