Analysis of Local Features for Handwritten Character Recognition

Seiichi Uchida; Marcus Liwicki
In: Proceedings of the 20th International Conference on Pattern Recognition. International Conference on Pattern Recognition (ICPR-2010), August 23-26, Istanbul, Turkey, Pages 1945-1948, IEEE, 2010.


This paper investigates a part-based recognition method of handwritten digits. In the proposed method the global structure of digit patterns is discarded by representing each pattern by just a set of local feature vectors. The method is then comprised of two steps. First, each of J partial patterns of a target pattern is recognized into one of ten categories (“0”–“9”) by the nearest neighbor discrimination with a large database of reference partial patterns. Second, the category of the target pattern is determined by the majority voting on the J local recognition results. Despite the pessimistic expectation, we have reached recognition rates much higher than 90% for the task of digit recognition.



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