Markov random fields for handwritten Chinese character recognition

Jia Zeng; Zhi-Qiang Liu

doi:10.1109/ICDAR.2005.158

Markov random fields for handwritten Chinese character recognition

Jia Zeng
, Zhi-Qiang Liu

School of Creative Media

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

17 Citations (Scopus)

Abstract

In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition. © 2005 IEEE.

Original language	English
Title of host publication	Proceedings of the Eighth International Conference on Document Analysis and Recognition
Publisher	IEEE Computer Society
Pages	101-105
Volume	2005
ISBN (Print)	0769524206, 9780769524207
DOIs	https://doi.org/10.1109/ICDAR.2005.158
Publication status	Published - 2005
Event	8th International Conference on Document Analysis and Recognition, ICDAR 2005 - Seoul, Korea, Republic of Duration: 31 Aug 2005 → 1 Sept 2005

Publication series

Name	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume	2005
ISSN (Print)	1520-5363

Conference

Conference	8th International Conference on Document Analysis and Recognition, ICDAR 2005
Place	Korea, Republic of
City	Seoul
Period	31/08/05 → 1/09/05

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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SDG 3 Good Health and Well-being

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Zeng, J., & Liu, Z.-Q. (2005). Markov random fields for handwritten Chinese character recognition. In Proceedings of the Eighth International Conference on Document Analysis and Recognition (Vol. 2005, pp. 101-105). Article 1575518 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2005). IEEE Computer Society. https://doi.org/10.1109/ICDAR.2005.158

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title = "Markov random fields for handwritten Chinese character recognition",

abstract = "In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition. {\textcopyright} 2005 IEEE.",

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Zeng, J & Liu, Z-Q 2005, Markov random fields for handwritten Chinese character recognition. in Proceedings of the Eighth International Conference on Document Analysis and Recognition. vol. 2005, 1575518, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 2005, IEEE Computer Society, pp. 101-105, 8th International Conference on Document Analysis and Recognition, ICDAR 2005, Seoul, Korea, Republic of, 31/08/05. https://doi.org/10.1109/ICDAR.2005.158

Markov random fields for handwritten Chinese character recognition. / Zeng, Jia; Liu, Zhi-Qiang.
Proceedings of the Eighth International Conference on Document Analysis and Recognition. Vol. 2005 IEEE Computer Society, 2005. p. 101-105 1575518 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2005).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Zeng, Jia

AU - Liu, Zhi-Qiang

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N2 - In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition. © 2005 IEEE.

AB - In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition. © 2005 IEEE.

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T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

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BT - Proceedings of the Eighth International Conference on Document Analysis and Recognition

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Markov random fields for handwritten Chinese character recognition

Abstract

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