| Summary: | 碩士 === 嶺東科技大學 === 資訊科技應用研究所 === 99 === This thesis proposes a license plate recognition system with image processing equipped with the hierarchical neural network architecture. The overall system can be divided into four modules: image preprocessing, zone localizing for license plae, character pattern segmentation, and character recognition. The zone localizing process uses the connected component method associated with the zone characteristic analysis and it can indicate the region of the license plate on entire car images. The character pattern segmentation uses the Radon transform to detect the tilt angle and the vertical projections for charterer segmentation from the captured license plate region. The character recognition module is implemented by the hierarchical back propgation neural network (BPN) architecture, where digit and letter patterns are separately recognized. In the digits’ recognition, a given matching threshold is given for scoring and labeling. The other unlabeled patterns then are sent to the letters’ recognition stage. The experimental results show that the proposed recognition system can achieve 98.9% correct rate; where the success rate of character recognition is 99.7% and the license plate locatlizing can reach the correction 98.4%. Thus, the proposed recognition system can be demostrated to be efficient and feasible in this study.
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