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

Ming Zhu, Zheru Chi, Xiaoping Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
Number of pages5
ISBN (Electronic)9789810475246, 9810475241
Publication statusPublished - 1 Jan 2002
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Orchid Country Club, Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002


Conference9th International Conference on Neural Information Processing, ICONIP 2002

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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