Automatic allograph selection and multiple expert classification for totally unconstrained handwritten character recognition

We introduce a new method for online character recognition based on the co-operation of two classifiers, respectively operating on static and dynamic character properties. Both classifiers use the nearest neighbour algorithm. References have been selected previously using an unsupervised clustering technique for selecting, in each character class, the most representative allographs. Several co-operation architectures are presented, from the easier (balanced sum of both classifier outputs) types to the most complicated (integrating neural network) one. The recognition improvement varies between 30% and 50% according to the merging technique implemented. We evaluate the performance of each method based on the recognition rate and speed. Results are presented on 62 different character classes, and more than 75000 examples are from the UNIPEN database