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Recognition of strong and weak connection models in continuous sign language

  • Harbin Institute of Technology
  • CAS - Institute of Computing Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A new method to recognize continuous sign language based on hidden Markov model is proposed. According to the dependence of linguistic context, connections between elementary subwords are classified as strong connection and weak connection. The recognition of strong connection is accomplished with the aid of subword trees, which describe the connection of subwords in each sign language word. In weak connection, the main problem is how to extract the best matched subwords and find their end-points with little help of context information. The proposed method improves the summing process of the Viterbi decoding algorithm which is constrained in every individual model, and compares the end score at each frame to find the ending frame of a subword. Experimental results show an accuracy of 70% for continuous sign sentences that comprise no more than 4 subwords.

Original languageEnglish
Title of host publicationProceedings - 16th International Conference on Pattern Recognition, ICPR 2002
EditorsR. Kasturi, D. Laurendeau, C. Suen, N. Ayache, K. Boyer, H. Bunke, H. Christensen, M. Kunt, G. Sanniti di Baja, L. Shapiro, Y. Shirai, R. Woodham
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-78
Number of pages4
ISBN (Electronic)0769516963
DOIs
StatePublished - 2002
Event16th International Conference on Pattern Recognition, ICPR 2002 - Quebec City, Canada
Duration: 11 Aug 200215 Aug 2002

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Conference

Conference16th International Conference on Pattern Recognition, ICPR 2002
Country/TerritoryCanada
CityQuebec City
Period11/08/0215/08/02

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