A new representation of character shape and its use in on-line character recognition by a self organizing map
The purpose of this study is to investigate a new representation of shape and its use in handwritten on-line 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 traditional Hudidean distance, functions such as the Kullback Leibler divergence and the Hellinger distance are investigated to evaluate similarity of feature vectors because these functions provide measures of distance between distributions. We report on extensive experiments using a database of on-line Arabic characters produced without constraints by a large number of writers. Comparative results show the representation relevance and the superior performance of the scheme.