A structural/statistical feature based vector for handwritten character recognition

Authors:
L. Heutte;T. Paquet;J. V. Moreau;Y. Lecourtier;C. Olivier
Affiliations:
PSI-La3i, UFR des Sciences et Techniques, Université de Rouen, F76821 Mont-Saint-Aignan Cedex, France;PSI-La3i, UFR des Sciences et Techniques, Université de Rouen, F76821 Mont-Saint-Aignan Cedex, France;Matra Systèmes & Information, Véélizy-Villacoublay, France;PSI-La3i, UFR des Sciences et Techniques, Université de Rouen, F76821 Mont-Saint-Aignan Cedex, France;PSI-La3i, UFR des Sciences et Techniques, Université de Rouen, F76821 Mont-Saint-Aignan Cedex, France
Venue:
Pattern Recognition Letters
Year:
1998

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Abstract

This paper describes the application of structural features to the statistical recognition of handwritten characters. It has been demonstrated that a complete description of the characters, based on the combination of seven different families of features, can be achieved and that the same general-purpose structural/statistical feature based vector thus defined proves efficient and robust on different categories of handwritten characters such as digits, uppercase letters and graphemes.