Handwritten character recognition by an adaptive fuzzy clustering algorithm

Amit Sharan, Sunanda Mitra

Research output: Contribution to conferencePaper

Abstract

Unconstrained handwritten characters pose a serious challenge to the development of a recognition algorithm. Many approaches have been studied over the years for such a recognition algorithm. We use an adaptive neuro-fuzzy clustering algorithm for classification and recognition of handwritten characters of a variety of styles and investigate the effectiveness of Fourier coefficients as representative features of handwritten characters in the presence of noise. Our results indicate that the adaptive clustering algorithm outperforms k-means clustering in handwritten character recognition for the same data representation. However some misclassifications cannot be avoided due to inherent problems associated with large variability in handwriting styles and the presence of excessive noise in practice.

Original languageEnglish
Pages1820-1824
Number of pages5
StatePublished - 1994
EventProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA
Duration: Jun 26 1994Jun 29 1994

Conference

ConferenceProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
CityOrlando, FL, USA
Period06/26/9406/29/94

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    Sharan, A., & Mitra, S. (1994). Handwritten character recognition by an adaptive fuzzy clustering algorithm. 1820-1824. Paper presented at Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3), Orlando, FL, USA, .