Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
A Cascaded Genetic Algorithm for Efficient Optimization and Pattern Matching
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
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Normalization can be used to absorb writing variations and distortions, simplify the recognition processing steps, and improve the recognition rate of a Chinese handwriting recognition system. In this study, a genetic algorithm approach to Chinese handwriting normalization is proposed. In the proposed approach, a generalized normalization transform is defined as a linearly weighted combination of several normalization transforms and then genetic algorithms (GA's) are used to determine the optimal set of weighting coefficients. Here the fitness function contains three proposed features representing the characteristics of Chinese characters, namely, stroke density variation (SDV), character area coverage (CAC), and centroid offset (CO). Experimental results show the feasibility of the proposed approach