Determining Optimal Filters for Binarization of Degraded Characters in Color Using Genetic Algorithms

This paper proposes a new binarization technique of characters in color using genetic algorithms (GA) to search for an optimal sequence of filters through a filter bank. The filter bank contains simple image processing filters as applied to one of the RGB color planes and logical/arithmetic operations between two color planes. First, we classify images of degraded characters extracted from the public ICDAR 2003 robust OCR dataset into several groups according to degradation categories. Then, in the learning stage, by selecting training samples from each degradation category we apply GA to the combinatorial optimization problem of determining a filter sequence that maximizes the average fitness value calculated between the filtered training samples and their respective target images ideally binarized by humans. Finally, in the testing stage, we apply the optimal filter sequence to binarization of remaining test samples. Experimental results show the promising ability of the proposed method against a variety of image degradation causes