DocumentCode :
311128
Title :
Integrated segmentation and recognition of connected handwritten characters with recurrent neural network
Author :
Lee, Seong-Whan ; Lee, Eung-Jae
Author_Institution :
Dept. of Comput. Sci., Korea Univ., Seoul, South Korea
Volume :
1
fYear :
1995
fDate :
14-16 Aug 1995
Firstpage :
413
Abstract :
In this paper, we propose an integrated segmentation and recognition method for recognizing connected handwritten characters with recurrent neural network. It has been developed to both integrate segmentation and recognition within a single recurrent neural network and recognize connected handwritten characters using the spatial dependencies in the images of connected handwritten characters. In order to verify the performance of the proposed method, experiments with the NIST database have been carried out and the performance of the proposed method has been compared with those of the previous integrated segmentation and recognition methods
Keywords :
character recognition; handwriting recognition; image recognition; recurrent neural nets; NIST database; connected handwritten characters; integrated recognition; integrated segmentation; performance; recurrent neural network; spatial dependencies; Character recognition; Computer science; Data preprocessing; Handwriting recognition; Image recognition; Image segmentation; NIST; Neural networks; Pattern recognition; Recurrent neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-8186-7128-9
Type :
conf
DOI :
10.1109/ICDAR.1995.599025
Filename :
599025
Link To Document :
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