On-line handwritten digit recognition based on trajectory and velocity modeling

The handwriting is one of the most familiar communication media. Pen based interface combined with automatic handwriting recognition offers a very easy and natural input method. The handwritten signal is on-line collected via a digitizing device, and it is classified as one pre-specified set of characters. The main techniques applied in our work include two fields of research. The first one consists of the modeling system of handwriting. In this area, we developed a novel method of the handwritten trajectory modeling based on elliptic and Beta representation. The second part of our work shows the implementation of a classifier consisting of the Multi-Layers Perception of Neural Networks (MLPNN) developed in a fuzzy concept. The training process of the recognition system is based on an association of the Self Organization Maps (SOM) with Fuzzy K-Nearest Neighbor Algorithms (FKNNA). To test the performance of our system we build 30,000 Arabic digits. The global recognition rate obtained by our recognition system is about 95.08%.