A neural predictive approach for on-line cursive script recognition

We present a neural prediction system for on-line writer-independent character recognition as a first step towards a word recognition system. The input feature vectors contain the pen trajectory information, recorded by a digitizing tablet. Each letter is modeled by a variable number of predictive neural networks, depending on its length. Successive parts of a letter are modeled by different multilayer neural networks, only transitions from each one to itself or to its right neighbors being permitted. To deal with the great variability of cursive handwriting, we introduce a holistic approach for both learning and recognition, combining neural networks and dynamic programming techniques. Our system is able to recognize strongly distorted and truncated letters, obtained by automatic segmentation of 10000 words from 10 different writers. Even on such databases, inappropriate to character recognition (letters in it were not recorded as handwritten isolated characters), quite good recognition rates are obtained.