A stochastic representation of cursive Chinese characters for on-line recognition

Tzren Ru Chou; Wen Tsuen Chen

doi:10.1016/S0031-3203(96)00102-1

A stochastic representation of cursive Chinese characters for on-line recognition

Tzren Ru Chou, Wen Tsuen Chen^*

^*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

In this paper, a stochastic representation of on-line Chinese characters of cursive style is proposed. A character in this representation is modeled by a sequence of concatenated stochastic curves, termed stochastic cubic Bézier curves (SCBC), with random noises. Furthermore, we also propose a curve alignment procedure to consistently match an input character with a stochastic reference one. Some classification experiments were performed. The stochastic approach is hardly sensitive to the characters with linked and degraded strokes; meanwhile, its recognition rate is higher than 95% even for deformed confusing characters.

Original language	English
Pages (from-to)	903-920
Number of pages	18
Journal	Pattern Recognition
Volume	30
Issue number	6
DOIs	https://doi.org/10.1016/S0031-3203(96)00102-1
Publication status	Published - 1997 Jun
Externally published	Yes

Keywords

Bézier curves
De Casteljau algorithm
Dynamic programming
Elastic matching
Mahalanobis distance
Maximum likelihood estimation
On-line character recognition

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/S0031-3203(96)00102-1

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title = "A stochastic representation of cursive Chinese characters for on-line recognition",

abstract = "In this paper, a stochastic representation of on-line Chinese characters of cursive style is proposed. A character in this representation is modeled by a sequence of concatenated stochastic curves, termed stochastic cubic B{\'e}zier curves (SCBC), with random noises. Furthermore, we also propose a curve alignment procedure to consistently match an input character with a stochastic reference one. Some classification experiments were performed. The stochastic approach is hardly sensitive to the characters with linked and degraded strokes; meanwhile, its recognition rate is higher than 95% even for deformed confusing characters.",

keywords = "B{\'e}zier curves, De Casteljau algorithm, Dynamic programming, Elastic matching, Mahalanobis distance, Maximum likelihood estimation, On-line character recognition",

author = "Chou, {Tzren Ru} and Chen, {Wen Tsuen}",

year = "1997",

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T1 - A stochastic representation of cursive Chinese characters for on-line recognition

AU - Chou, Tzren Ru

AU - Chen, Wen Tsuen

PY - 1997/6

Y1 - 1997/6

N2 - In this paper, a stochastic representation of on-line Chinese characters of cursive style is proposed. A character in this representation is modeled by a sequence of concatenated stochastic curves, termed stochastic cubic Bézier curves (SCBC), with random noises. Furthermore, we also propose a curve alignment procedure to consistently match an input character with a stochastic reference one. Some classification experiments were performed. The stochastic approach is hardly sensitive to the characters with linked and degraded strokes; meanwhile, its recognition rate is higher than 95% even for deformed confusing characters.

AB - In this paper, a stochastic representation of on-line Chinese characters of cursive style is proposed. A character in this representation is modeled by a sequence of concatenated stochastic curves, termed stochastic cubic Bézier curves (SCBC), with random noises. Furthermore, we also propose a curve alignment procedure to consistently match an input character with a stochastic reference one. Some classification experiments were performed. The stochastic approach is hardly sensitive to the characters with linked and degraded strokes; meanwhile, its recognition rate is higher than 95% even for deformed confusing characters.

KW - Bézier curves

KW - De Casteljau algorithm

KW - Dynamic programming

KW - Elastic matching

KW - Mahalanobis distance

KW - Maximum likelihood estimation

KW - On-line character recognition

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DO - 10.1016/S0031-3203(96)00102-1

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SN - 0031-3203

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EP - 920

JO - Pattern Recognition

JF - Pattern Recognition

IS - 6

ER -

A stochastic representation of cursive Chinese characters for on-line recognition

Abstract

Keywords

ASJC Scopus subject areas

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