Offline recognition of chinese handwriting by multifeature and multilevel classification

Yuan Y. Tang; Lo Ting Tu; Jiming LIU; Seong Whan Lee; Win Win Lin; Ing Shyh Shyu

doi:10.1109/34.682186

Offline recognition of chinese handwriting by multifeature and multilevel classification

Yuan Y. Tang^*, Lo Ting Tu, Jiming LIU, Seong Whan Lee, Win Win Lin, Ing Shyh Shyu

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

78 Citations (Scopus)

Abstract

One of the most challenging topics is the recognition of Chinese handwriting, especially offline recognition. In this paper, an offline recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector, are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.

Original language	English
Pages (from-to)	556-561
Number of pages	6
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	20
Issue number	5
DOIs	https://doi.org/10.1109/34.682186
Publication status	Published - 1998

Scopus Subject Areas

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

User-Defined Keywords

Gaussian distribution selector
Multifeature
Multilevel classification
Offline chinese handwriting recognition
Overlap clustering

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10.1109/34.682186

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title = "Offline recognition of chinese handwriting by multifeature and multilevel classification",

abstract = "One of the most challenging topics is the recognition of Chinese handwriting, especially offline recognition. In this paper, an offline recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector, are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.",

keywords = "Gaussian distribution selector, Multifeature, Multilevel classification, Offline chinese handwriting recognition, Overlap clustering",

author = "Tang, {Yuan Y.} and Tu, {Lo Ting} and Jiming LIU and Lee, {Seong Whan} and Lin, {Win Win} and Shyu, {Ing Shyh}",

note = "Funding Information: This work was supported by research grants received from Research Grant Council (RGC) of Hong Kong and Faculty Research Grant (FRG) of Hong Kong Baptist University. This work was also a partial result of the project no. 35N1300 conducted by the Industrial Technology Research Institute under the sponsorship of the Minister of Economic Affairs, Republic of China, and the Hallym Academy of Science, Hallym University, Korea.",

year = "1998",

doi = "10.1109/34.682186",

language = "English",

volume = "20",

pages = "556--561",

journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",

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T1 - Offline recognition of chinese handwriting by multifeature and multilevel classification

AU - Tang, Yuan Y.

AU - Tu, Lo Ting

AU - LIU, Jiming

AU - Lee, Seong Whan

AU - Lin, Win Win

AU - Shyu, Ing Shyh

N1 - Funding Information: This work was supported by research grants received from Research Grant Council (RGC) of Hong Kong and Faculty Research Grant (FRG) of Hong Kong Baptist University. This work was also a partial result of the project no. 35N1300 conducted by the Industrial Technology Research Institute under the sponsorship of the Minister of Economic Affairs, Republic of China, and the Hallym Academy of Science, Hallym University, Korea.

PY - 1998

Y1 - 1998

N2 - One of the most challenging topics is the recognition of Chinese handwriting, especially offline recognition. In this paper, an offline recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector, are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.

AB - One of the most challenging topics is the recognition of Chinese handwriting, especially offline recognition. In this paper, an offline recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector, are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.

KW - Gaussian distribution selector

KW - Multifeature

KW - Multilevel classification

KW - Offline chinese handwriting recognition

KW - Overlap clustering

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JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

IS - 5

ER -

Offline recognition of chinese handwriting by multifeature and multilevel classification

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