A note on binary template matching
Pattern Recognition
Binarization and multithresholding of document images using connectivity
CVGIP: Graphical Models and Image Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential evolution for optimizing the hybrid filter combination in image edge enhancement
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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Optimal binarization of degraded grayscale characters is a crucial step to subsequent character recognition. This paper proposes a new, promising binalization technique of grayscale characters using genetic algorithms (GA) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. First, we classify degraded samples of grayscale characters into several categories. Then, in the learning stage, by selecting a training sample from each degradation category we apply GA to the combinatorial optimization problem of determining a sequence of filters that maximizes the fitness value between the filtered training sample and its target image ideally binarized by humans. Finally, in the testing stage, we apply the optimal sequence of filters thus obtained to remaining test samples for each degradation category. Experiments using the public ICDAR 2003 robust OCR dataset demonstrate promising results of binarization of grayscale characters against a wide variety of degradation causes.