HMM-based Online Handwritten Gurmukhi Character Recognition

• Autorzy: Sharma A. , Kumar R. , Sharma R. K.
• Języki publikacji: EN

Abstrakty

EN

This paper presents a hidden Markov model-based online handwritten character recognition for Gurmukhi script. We discuss a procedure to develop a hidden Markov model database in order to recognize Gurmukhi characters. A test with 60 handwritten samples, where each sample includes 41 Gurmukhi characters, shows a 91.95% recognition rate, and an average recognition speed of 0.112 seconds per stroke. The hidden Markov model database has been developed in XML using 5330 Gurmukhi characters. This work shall be useful to implement a hidden Markov model in online handwriting recognition and its software development.

Słowa kluczowe

EN

handwriting recognition; preprocessing; feature extraction; infrastructure subsystem

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2010
• Tom: Vol. 19, No. 4
• Strony: 439--449
• Opis fizyczny: Bibliogr. 15 poz., wykr.

Twórcy

autor

Sharma A.

autor

Kumar R.

autor

Sharma R. K.

• Department of Mathematics, Panjab University, Chandigarh - 160114, India,

Bibliografia

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