Presentations (Communicative Events)

Online Arabic Handwriting Recognition Using Hidden Markov Models

Biadsy, Fadi; El-Sana, Jihad; Habash, Nizar Y.

Online handwriting recognition of Arabic script is a difficult problem since it is naturally both cursive and unconstrained. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. This paper introduces a Hidden Markov Model (HMM) based system to provide solutions for most of the difficulties inherent in recognizing Arabic script including: letter connectivity, position-dependent letter shaping, and delayed strokes. This is the first HMM-based solution to online Arabic handwriting recognition. We report successful results for writer-dependent and writer-independent word recognition.


More About This Work

Academic Units
Computer Science
Proceedings of the 10th International Workshop on Frontiers of Handwriting and Recognition
Published Here
June 28, 2013