Abstract
In this paper, a method based on the Distribution of Character Skeleton is adopted to extract the structural features of handwriting image. In this method, we firstly extract the character skeleton by applying morphology and then compute the skeleton direction distribution in each sub-region as writing style logos of different writers. Comparing with Gabor texture analysis method, it demonstrates the feasibility and effectiveness of this method. We adopt Nearest neighbor classifier based on weighted, also the classification results verified the classification performance is better than Gabor texture analysis method and the correct identification rate is higher.
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© 2012 Springer Science+Business Media Dordrecht
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Wang, Y., Zhang, D., Luo, W. (2012). Writer Identification Based on the Distribution of Character Skeleton. In: Wu, Y. (eds) Advanced Technology in Teaching - Proceedings of the 2009 3rd International Conference on Teaching and Computational Science (WTCS 2009). Advances in Intelligent and Soft Computing, vol 117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25437-6_43
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DOI: https://doi.org/10.1007/978-3-642-25437-6_43
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