Stochastic model of stroke order variation

Yoshinori Katayama; Seiichi Uchida; Hiroaki Sakoe

doi:10.1109/ICDAR.2009.146

Stochastic model of stroke order variation

Yoshinori Katayama, Seiichi Uchida, Hiroaki Sakoe

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

4 Citations (Scopus)

Abstract

A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order. The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.

Original language	English
Title of host publication	ICDAR2009 - 10th International Conference on Document Analysis and Recognition
Pages	803-807
Number of pages	5
DOIs	https://doi.org/10.1109/ICDAR.2009.146
Publication status	Published - 2009
Event	ICDAR2009 - 10th International Conference on Document Analysis and Recognition - Barcelona, Spain Duration: Jul 26 2009 → Jul 29 2009

Other

Other	ICDAR2009 - 10th International Conference on Document Analysis and Recognition
Country/Territory	Spain
City	Barcelona
Period	7/26/09 → 7/29/09

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition

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10.1109/ICDAR.2009.146

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title = "Stochastic model of stroke order variation",

abstract = "A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order. The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.",

author = "Yoshinori Katayama and Seiichi Uchida and Hiroaki Sakoe",

year = "2009",

doi = "10.1109/ICDAR.2009.146",

language = "English",

isbn = "9780769537252",

pages = "803--807",

booktitle = "ICDAR2009 - 10th International Conference on Document Analysis and Recognition",

note = "ICDAR2009 - 10th International Conference on Document Analysis and Recognition ; Conference date: 26-07-2009 Through 29-07-2009",

}

TY - GEN

T1 - Stochastic model of stroke order variation

AU - Katayama, Yoshinori

AU - Uchida, Seiichi

AU - Sakoe, Hiroaki

PY - 2009

Y1 - 2009

N2 - A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order. The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.

AB - A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order. The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.

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U2 - 10.1109/ICDAR.2009.146

DO - 10.1109/ICDAR.2009.146

M3 - Conference contribution

AN - SCOPUS:71249145228

SN - 9780769537252

SP - 803

EP - 807

BT - ICDAR2009 - 10th International Conference on Document Analysis and Recognition

T2 - ICDAR2009 - 10th International Conference on Document Analysis and Recognition

Y2 - 26 July 2009 through 29 July 2009

ER -

Stochastic model of stroke order variation

Abstract

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All Science Journal Classification (ASJC) codes

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