Gabor-based recognizer for chinese handwriting from segmentation-free strategy

Tong Hua Su; Tian Wen Zhang; De Jun Guan; Hu Jie Huang

doi:10.1007/978-3-540-74272-2_67

Gabor-based recognizer for chinese handwriting from segmentation-free strategy

Tong Hua Su^*
, Tian Wen Zhang
, De Jun Guan
, Hu Jie Huang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

1 Scopus citations

Abstract

Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segment at ion-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings
Publisher	Springer Verlag
Pages	539-546
Number of pages	8
ISBN (Print)	9783540742715
DOIs	https://doi.org/10.1007/978-3-540-74272-2_67
State	Published - 2007
Externally published	Yes
Event	12th International Conference on Computer Analysis of Images and Patterns, CAIP 2007 - Vienna, Austria Duration: 27 Aug 2007 → 29 Aug 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4673 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Computer Analysis of Images and Patterns, CAIP 2007
Country/Territory	Austria
City	Vienna
Period	27/08/07 → 29/08/07

Keywords

Chinese handwriting recognition
Gabor filter
HMM

Access to Document

10.1007/978-3-540-74272-2_67

Cite this

Su, T. H., Zhang, T. W., Guan, D. J., & Huang, H. J. (2007). Gabor-based recognizer for chinese handwriting from segmentation-free strategy. In Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings (pp. 539-546). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4673 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-74272-2_67

Su, Tong Hua ; Zhang, Tian Wen ; Guan, De Jun et al. / Gabor-based recognizer for chinese handwriting from segmentation-free strategy. Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings. Springer Verlag, 2007. pp. 539-546 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Gabor-based recognizer for chinese handwriting from segmentation-free strategy",

abstract = "Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segment at ion-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.",

keywords = "Chinese handwriting recognition, Gabor filter, HMM",

author = "Su, \{Tong Hua\} and Zhang, \{Tian Wen\} and Guan, \{De Jun\} and Huang, \{Hu Jie\}",

year = "2007",

doi = "10.1007/978-3-540-74272-2\_67",

language = "英语",

isbn = "9783540742715",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "539--546",

booktitle = "Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings",

address = "德国",

note = "12th International Conference on Computer Analysis of Images and Patterns, CAIP 2007 ; Conference date: 27-08-2007 Through 29-08-2007",

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Su, TH, Zhang, TW, Guan, DJ & Huang, HJ 2007, Gabor-based recognizer for chinese handwriting from segmentation-free strategy. in Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4673 LNCS, Springer Verlag, pp. 539-546, 12th International Conference on Computer Analysis of Images and Patterns, CAIP 2007, Vienna, Austria, 27/08/07. https://doi.org/10.1007/978-3-540-74272-2_67

Gabor-based recognizer for chinese handwriting from segmentation-free strategy. / Su, Tong Hua; Zhang, Tian Wen; Guan, De Jun et al.
Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings. Springer Verlag, 2007. p. 539-546 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4673 LNCS).

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

TY - GEN

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AU - Guan, De Jun

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N2 - Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segment at ion-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.

AB - Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segment at ion-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.

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Su TH, Zhang TW, Guan DJ, Huang HJ. Gabor-based recognizer for chinese handwriting from segmentation-free strategy. In Computer Analysis of Images and Patterns - 12th International Conference, CAIP 2007, Proceedings. Springer Verlag. 2007. p. 539-546. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-74272-2_67

Gabor-based recognizer for chinese handwriting from segmentation-free strategy

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