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

Authors:
Yousri Kessentini;Thierry Paquet;AbdelMajid Ben Hamadou
Affiliations:
Laboratoire LITIS EA 4108, Université de Rouen France, Site du Madrillet, 76800 Saint-Etienne du Rouvray, France and Laboratoire MIRACL, Université de Sfax Tunisie, Route de Tunis km10, ...;Laboratoire LITIS EA 4108, Université de Rouen France, Site du Madrillet, 76800 Saint-Etienne du Rouvray, France;Laboratoire MIRACL, Université de Sfax Tunisie, Route de Tunis km10, B.P. No. 242-3021 Sfax, Tunisia
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

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.