Segmentation and recognition of handwritten Pitman shorthand outlines using neural networks

Ming Zhu, Zheru Chi, Xiao Ping Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

An approach for segmentation and recognition of on-line vocalized outlines of Pitman shorthand is presented. The approach is to use a trained neural network for the segmentation of the vocalized outlines for the detection of over-segmentation; and to use another trained neural network for the recognition of Pitman shorthand consonant signs; while the word recognition was based on the estimation of the overall confidence on the stroke classification. Experimental results on a test set containing 68 most frequently used English words showed that on average, the approach can achieve an accuracy rate of 89.6%.
Original languageEnglish
Pages (from-to)532-536+550
JournalZhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science)
Volume37
Issue number5
Publication statusPublished - 1 Sep 2003
Externally publishedYes

Keywords

  • Handwriting recognition
  • Neural networks
  • Shorthand

ASJC Scopus subject areas

  • Engineering(all)

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