Segmentation and recognition of on-line Pitman shorthand outlines using neural networks

Ming Zhu; Zheru Chi; Xiaoping Wang

doi:10.1109/ICONIP.2002.1201935

Segmentation and recognition of on-line Pitman shorthand outlines using neural networks

Ming Zhu
, Zheru Chi
, Xiaoping Wang

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural network, (2) the recognition of Pitman shorthand consonant signs using another neural network, and (3) the word recognition based on the estimation of the overall confidence on the stroke classification. Experimental results on a small test set containing 68 most frequently used English words are reported in the paper. The average accuracy on these test words can reaches 89.6% by using our approach.

Original language	English
Title of host publication	ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publication	Computational Intelligence for the E-Age
Publisher	IEEE
Pages	2454-2458
Number of pages	5
Volume	5
ISBN (Electronic)	9789810475246, 9810475241
DOIs	https://doi.org/10.1109/ICONIP.2002.1201935
Publication status	Published - 1 Jan 2002
Event	9th International Conference on Neural Information Processing, ICONIP 2002 - Orchid Country Club, Singapore, Singapore Duration: 18 Nov 2002 → 22 Nov 2002

Conference

Conference	9th International Conference on Neural Information Processing, ICONIP 2002
Country/Territory	Singapore
City	Singapore
Period	18/11/02 → 22/11/02

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

More information

10.1109/ICONIP.2002.1201935

Cite this

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title = "Segmentation and recognition of on-line Pitman shorthand outlines using neural networks",

abstract = "This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural network, (2) the recognition of Pitman shorthand consonant signs using another neural network, and (3) the word recognition based on the estimation of the overall confidence on the stroke classification. Experimental results on a small test set containing 68 most frequently used English words are reported in the paper. The average accuracy on these test words can reaches 89.6\% by using our approach.",

author = "Ming Zhu and Zheru Chi and Xiaoping Wang",

year = "2002",

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doi = "10.1109/ICONIP.2002.1201935",

language = "English",

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booktitle = "ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing",

publisher = "IEEE",

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Zhu, M, Chi, Z & Wang, X 2002, Segmentation and recognition of on-line Pitman shorthand outlines using neural networks. in ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. vol. 5, 1201935, IEEE, pp. 2454-2458, 9th International Conference on Neural Information Processing, ICONIP 2002, Singapore, Singapore, 18/11/02. https://doi.org/10.1109/ICONIP.2002.1201935

Segmentation and recognition of on-line Pitman shorthand outlines using neural networks. / Zhu, Ming; Chi, Zheru; Wang, Xiaoping.
ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. Vol. 5 IEEE, 2002. p. 2454-2458 1201935.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Chi, Zheru

AU - Wang, Xiaoping

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N2 - This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural network, (2) the recognition of Pitman shorthand consonant signs using another neural network, and (3) the word recognition based on the estimation of the overall confidence on the stroke classification. Experimental results on a small test set containing 68 most frequently used English words are reported in the paper. The average accuracy on these test words can reaches 89.6% by using our approach.

AB - This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural network, (2) the recognition of Pitman shorthand consonant signs using another neural network, and (3) the word recognition based on the estimation of the overall confidence on the stroke classification. Experimental results on a small test set containing 68 most frequently used English words are reported in the paper. The average accuracy on these test words can reaches 89.6% by using our approach.

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U2 - 10.1109/ICONIP.2002.1201935

DO - 10.1109/ICONIP.2002.1201935

M3 - Conference article published in proceeding or book

VL - 5

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BT - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing

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T2 - 9th International Conference on Neural Information Processing, ICONIP 2002

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ER -

Segmentation and recognition of on-line Pitman shorthand outlines using neural networks

Abstract

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