Integrated segmentation and recognition of connected handwritten characters with recurrent neural network

Seong Whan Lee, Eung Jae Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Pages251-261
Number of pages11
Volume2660
ISBN (Print)0819420344, 9780819420343
Publication statusPublished - 1996
EventDocument Recognition III - San Jose, CA, USA
Duration: 1996 Jan 291996 Jan 30

Other

OtherDocument Recognition III
CitySan Jose, CA, USA
Period96/1/2996/1/30

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Lee, S. W., & Lee, E. J. (1996). Integrated segmentation and recognition of connected handwritten characters with recurrent neural network. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 2660, pp. 251-261). Society of Photo-Optical Instrumentation Engineers.