A Study On The Improvement Of Vehicle Plate Recognition

Abstract

Camera-captured car plate images contain much variation and noise and the character images in a plate are typically very small. We attempted to improve the plate identification efficiency suitable for this undesirable condition. We experimented various image preprocessing and feature extracting methods and the very effective features that can compensate one feature's limitation is determined through extensive experiments. Finally two very effective features that can complement the limitations of each other feature(classifier) are determined and the efficiency is proved by recognition experiments. This approach is very necessary when handling plate character images which are typically small, various, and noisy. Individual classification result, confidence factor, region name relation and feedback verification are comprehensively considered to enhance the overall recognition efficiency. The efficiency of our method is verified by a recognition experiment using real car plate images taken from traffic roads.