A Simple Method for Chinese License Plate Recognition Based on Support Vector Machine
We present a simple method based on Support Vector Machine (SVM) for Chinese license plate recognition. By firstly pre-processing the input images containing license plates, a set of normalized subimages can be obtained, each of which contains a number, an English letter or a Chinese character. We then transform these subimages into vectors by simply using pixel values. In this way, we can avoid the problem of excessive dependency on feature extraction during recognition. Next, scaling and cross-validation are performed to eliminate outliers and find the best parameters for the SVM model. We use real color images captured at a motorway toll in our experiments. Be compared with previous work based on neural network, the SVM-based method produces a higher correct recognition rate. Experimental results also show the superiority of the SVM-based method when only a small number of samples are available.
Xiaojun Chi Junyu Dong Aihua Liu Huiyu Zhou
Department of Computer Science, Ocean University of China, Qingdao, 266071 Qingdao Qianwan Container Terminals Co., Ltd, 266500 Department of Computer Science, University of Essex, UK CO4 3SQ
国际会议
2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)
广西桂林
英文
2141-2145
2006-06-25(万方平台首次上网日期,不代表论文的发表时间)