Handwriting prediction based character recognition using recurrent neural network

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

doi:10.1109/ICSMC.2011.6084060

Handwriting prediction based character recognition using recurrent neural network

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 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 language	English
Title of host publication	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages	2549-2554
Number of pages	6
DOIs	https://doi.org/10.1109/ICSMC.2011.6084060
Publication status	Published - 2011 Dec 23
Externally published	Yes
Event	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States Duration: 2011 Oct 9 → 2011 Oct 12

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Other

Other	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Country/Territory	United States
City	Anchorage, AK
Period	11/10/9 → 11/10/12

Keywords

Neural Networks
Prediction based Recognition

ASJC Scopus subject areas

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

Access to Document

10.1109/ICSMC.2011.6084060

Cite this

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

Handwriting prediction based character recognition using recurrent neural network. / Nishide, Shun; Okuno, Hiroshi G.; Ogata, Tetsuya et al.
2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest. 2011. p. 2549-2554 6084060 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nishide, S, Okuno, HG, Ogata, T & Tani, J 2011, Handwriting prediction based character recognition using recurrent neural network. in 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest., 6084060, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 2549-2554, 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011, Anchorage, AK, United States, 11/10/9. https://doi.org/10.1109/ICSMC.2011.6084060

Nishide S, Okuno HG, Ogata T, Tani J. Handwriting prediction based character recognition using recurrent neural network. In 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest. 2011. p. 2549-2554. 6084060. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/ICSMC.2011.6084060

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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.",

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AB - 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.

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Handwriting prediction based character recognition using recurrent neural network

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