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

In this communication, we introduce a new method for on-line 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 all unsupervised clustering technic selecting, in each character class, the most representative allographs. Several co-operation architectures (already tested in OCR but neither in online recognition) are presented, from the easier (balanced sum of both classifier outputs) to the most complicated (Integrating neural network). the recognition improvement varies between 30% and 50% according to the merging technic implemented. We try to appreciate each method asset oil recognition rate and speed. Results are presented on 62 different character classes, and more than 75000 examples from UNIPEN database.