Abstract
Offline Chinese characters segmentation is one of the most difficult problems in Chinese character recognition, because handwritten Chinese characters have deformations, connected strokes and overlapped characters, and they often occurre with punctuations and digital numrbers. The multi-step offline handwritten Chinese characters segmentation method based on adaptive genetic algorithm was put forward in this paper to segment connected or overlaps characters, punctuations and digital numbers. Genetic algorithm chose the optimal threshold of projection profile histogram method to segment character string roughly. Genetic algorithm parameters were adaptively chosen according to different character string images. Then, an estimation strategy determines whether the character block to be merged If the character block contained character, then it would be merged with neighbor character block that had the shortest distance to it. Finally, Viterbi algorithm re-segmented the insufficient segmented characters. Original rule 4 was modified and a genetic algorithm rules were put forward to delete redundant paths. Experiments on HIW-MW database shows that the new algorithm has correct segmentation rate of 74.55% which is higher than other two segment methods. The new algorithm can segment Chinese characters, punctuation and digital numbers correctly, and it is an efficient segment method.
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Zheng, Rr., Zhao, Jy., Wu, Bc. (2009). Multi-step Offline Handwritten Chinese Characters Segmentation with GA. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_1
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DOI: https://doi.org/10.1007/978-3-642-03664-4_1
Publisher Name: Springer, Berlin, Heidelberg
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