Abstract
Letter and number recognition in license plates is widely considered a solved problem in many practical license plate recognition (LPR) systems. However, Chinese character recognition for LPR application still faces many challenges, such as more complex structure, defective character, partial occlusion, and sensitiveness to affine distortion, noise, scaling, illumination variation, contamination, blurring, and so no. In this paper, a novel method of Chinese character recognition is proposed, based on SIFT feature points clustering and matching in which a center matching strategy is designed to improve recognition efficiency. Promising experimental results demonstrate that the proposed is robust to the previous adverse factors in natural scenes and acquires higher efficiency that may meet requirements in practical application.
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