Off-line handwritten word recognition using multi-stream hidden Markov models

Yousri Kessentini (1) , Thierry Paquet (1) , Abdelmajid Benhamadou (2)

1 DocApp - LITIS - Equipe Apprentissage
2 MIRACL - Multimedia, InfoRmation systems and Advanced Computing Laboratory

In this paper, we present a multi-stream approach for off-line handwritten word recognition. The proposed approach combines low level feature streams namely, density based features extracted from 2 different sliding windows with different widths, and contour based features extracted from upper and lower contours. The multi-stream paradigm provides an interesting framework for the integration of multiple sources of information and is compared to the standard combination strategies namely fusion of representations and fusion of decisions. We investigate the extension of 2-stream approach to N streams (N = 2, ... , 4) and analyze the improvement in the recognition performance. The computational cost of this extension is discussed. Significant experiments have been carried out on two publicly available word databases: IFN/ENIT benchmark database (Arabic script) and IRONOFF database (Latin script). The multi-stream framework improves the recognition performance in both cases. Using 2-stream approach, the best recognition performance is 79.8%, in the case of the Arabic script, on a 2100-word lexicon consisting of 946 Tunisian town/village names. In the case of the Latin script, the proposed approach achieves a recognition rate of 89.8% using a lexicon of 196 words.

Mots clés: Off-line handwriting recognition; Hidden Markov models; Latin script; Arabic script; Multi-stream; Information combination

Domaines: Traitement du texte et du document

Yousri Kessentini, Thierry Paquet, Abdelmajid Benhamadou. Off-line handwritten word recognition using multi-stream hidden Markov models. Pattern Recognition Letters, 2010, 31 (1), pp.60 - 70. ⟨10.1016/j.patrec.2009.08.009⟩. ⟨hal-00668271⟩