Off-line cursive word recognition with a hybrid neural-HMM system

Abstract

In a recent publication [1], we have introduced a neural predictive system for on-line word recognition. Our approach implements a Hidden Markov Model (HMM)-based cooperation of several predictive neural networks. The task of the HMM is to guide the training procedure of neural networks on successive parts of a word. Each word is modeled by the concatenation of letter-models corresponding to the letters composing it. Successive parts of a word are this way modeled by different neural networks. A dynamical segmentation allows to adjust letter-models to the great variability of handwriting encountered in the words. Our system combines Multilayer Neural Networks and Dynamic Programming with an underlying Left-Right Hidden Markov Model (HMM). In this paper, we present an extension of this model to off-line word recognition. We use on-line data in these off-line experiments, generating a binary image from trajectory data. The feature extraction module then turns each binary image into a sequence of feature vectors, called ‘frames’, combining low-level and high-level features in a new feature extraction paradigm. Some results for word recognition are presented.KeywordsHide Markov ModelWord RecognitionRecognition RateOptimal PathCurrent FrameThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.