Application independent localisation of vehicle plate number using multi-window-size binarisation and semi-hybrid genetic algorithm

Abstract

This study introduces an enhanced version of a previously published genetic algorithm based technique to allow fast and accurate detection of the vehicle plate number independently of the used application. Hence, significant enhancements are introduced to upgrade the genetic algorithm into a semi-hybrid category by preceding it with a sorting and subgrouping process that reduces the search space and following it by a rule-based local search to optimize its output. The updated population structure and its initiation with subsets of the sorted image foreground objects results in a linear relationship between the image complexity represented by the number of foreground objects and the genetic algorithm search space instead of being exponential in previous versions. Moreover, a novel extra variable-size-window adaptive binarisation step is introduced to overcome the problems of attached license symbols that cannot be solved by the skipping ability introduced in previous versions. Various image samples with a wide range in scale, orientation, and symbol connectivity have been experimented to verify the effects of the new improvements. Encouraging results with 99.2% detection accuracy have been reported using a public dataset outperforming the author of the dataset by more than 5.5% and the state of the art systems by 2%.