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Proceedings Paper

Integrated segmentation and recognition of connected handwritten characters with recurrent neural network
Author(s): Seong-Whan Lee; Eung-Jae Lee
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Paper Abstract

In this paper, we propose an efficient method for integrated segmentation and recognition of connected handwritten characters with recurrent neural network. In the proposed method, a new type of recurrent neural network is developed for training the spatial dependencies in connected handwritten characters. This recurrent neural network differs from Jordan's and Elman's recurrent networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. In order to verify the performance of the proposed method, experiments with the NIST database have been performed and the performance of the proposed method has been compared with those of the previous integrated segmentation and recognition methods. The experimental results reveal that the proposed method is superior to the previous integrated segmentation and recognition methods in view of discrimination and generalization ability.

Paper Details

Date Published: 7 March 1996
PDF: 11 pages
Proc. SPIE 2660, Document Recognition III, (7 March 1996); doi: 10.1117/12.234707
Show Author Affiliations
Seong-Whan Lee, Korea Univ. (South Korea)
Eung-Jae Lee, Chungbuk National Univ. (South Korea)


Published in SPIE Proceedings Vol. 2660:
Document Recognition III
Luc M. Vincent; Jonathan J. Hull, Editor(s)

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