Normalisation of handwriting speed for online Arabic characters recognition

Abstract

In this paper, we propose a new solution for the problem of the unevenly distributing points along the stroke curve, in online Arabic handwriting recognition, due to the variation in writing speed. An algorithm based on linear interpolation is generally used to solve this problem. The main weakness of this algorithm is the missing of some information related to point density distributions in different stroke parts. This limitation is due to the use of only one re-sampling distance. In our approach, we propose to segment the stroke trajectory into several parts according to their densities and to classify them into three classes: high, medium and low density. Three re-sampling distances, instead of one, are suggested, each of them is associated to one class of densities. This solution is evaluated using NOUN dataset and it gives an excellent improvement in the recognition rate, up to 4%, compared to linear interpolation method.