Handwriting prediction based character recognition using recurrent neural network

Shun Nishide, Hiroshi G. Okuno, Tetsuya Ogata, Jun Tani

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

6 Citations (Scopus)

Abstract

Humans are said to unintentionally trace handwriting sequences in their brains based on handwriting experiences when recognizing written text. In this paper, we propose a model for predicting handwriting sequence for written text recognition based on handwriting experiences. The model is first trained using image sequences acquired while writing text. The image features of sequences are self-organized from the images using Self-Organizing Map. The feature sequences are used to train a neuro-dynamics learning model. For recognition, the text image is input into the model for predicting the handwriting sequence and recognition of the text. We conducted two experiments using ten Japanese characters. The results of the experiments show the effectivity of the model.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages2549-2554
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK
Duration: 2011 Oct 92011 Oct 12

Other

Other2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
CityAnchorage, AK
Period11/10/911/10/12

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Keywords

  • Neural Networks
  • Prediction based Recognition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Cite this

Nishide, S., Okuno, H. G., Ogata, T., & Tani, J. (2011). Handwriting prediction based character recognition using recurrent neural network. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2549-2554). [6084060] https://doi.org/10.1109/ICSMC.2011.6084060