Abstract
In LPR system, character recognition subsystem is heavily affected by image quality. To resolve this problem and improve recognition rate, a new algorithm is proposed, in which pulse coupled neural network (PCNN) is applied into the recognition of license plate character. PCNN model is simplified to improve computation efficiency, and then is utilized to extract three features from dimension-normalized binary result of input character image. Based on these features, weighted voting is performed and final estimation of input character is made. The experiment results show that compared with common algorithms based on BP network, the new algorithm based on simplified PCNN model has higher total recognition rate and stronger robustness, and is more convenient and flexible.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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