A new representation of shape and its use for high performance in online Arabic character recognition by an associative memory

Authors:
N. Mezghani;A. Mitiche;M. Cheriet
Affiliations:
INRS Énergie, Matériaux et Télécommunications and Laboratoire d'imagerie, de vision et d'intelligence artificielle, École de Technologie Supérieure, 1100 ...;INRS Énergie, Matériaux et Télécommunications, 800 de la Gauchetière Ouest, Suite 6900, H5A 1K6, Montréal, QC, Canada;Laboratoire d'imagerie, de vision et d'intelligence artificielle, École de Technologie Supérieure, 1100 rue Notre-Dame Ouest, H3C 1K3, Montréal QC, Canada
Venue:
International Journal on Document Analysis and Recognition
Year:
2005

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Abstract

The purpose of this study is to investigate a new representation of shape and its use in handwritten online character recognition by a Kohonen associative memory. This representation is based on the empirical distribution of features such as tangents and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a Kohonen neural network trained using the representation. In addition to the Euclidean distance traditionally used in the Kohonen training algorithm to measure the similarities among feature vectors, we also investigate the Kullback–Leibler divergence and the Hellinger distance, functions that measure distance between distributions. Furthermore, we perform operations (pruning and filtering) on the trained memory to improve its classification potency. We report on extensive experiments using a database of online Arabic characters produced without constraints by a large number of writers. Comparative results show the pertinence of the representation and the superior performance of the scheme.