Accented Handwritten Character Recognition Using SVM - Application to French

This paper deals with the problem of recognizing accented and non-accented characters in French handwriting. Accented characters increase the number of classes to be recognized. The performances of powerful classifier such as SVM are declined by the presence of accents. In this paper, an accented character is segmented into two parts: the root character or letter and the accent. These two parts are recognized separately, and the results are combined to rebuild the accented character. This approach avoids the combination of characters and accents that causes an increase in the number of classes to be considered. For handwritten character recognition, the combination of on-line and off-line features is used. The paper illustrates that French accented and non-accented characters and digits can be described by a combination of this kind of data. Moreover, the number of features of the combination is not necessarily very high. The experimental investigations show that the handwritten character recognition built on 45 selected features can compete with recognition rate and response time of other well known tested on standard databases such as UNIPEN and IRONOFF.