OFF-LINE HANDWRITING RECOGNITION USING VARIOUS HYBRID MODELING TECHNIQUES AND CHARACTER N-GRAMS

In this paper a system for on-line cursive handwriting recognition is described. The system is based on Hidden Markov Models (HMMs) using discrete and hybrid modeling techniques. Here, we focus on two aspects of the recognition system. First, we present different hybrid modeling techniques, whereas one depends on an information theory-based neural network (MMI-criterion) used as a vector quantizer and the other uses a neural net for estimating the a posteriori probabilities to replace the codebook of a tied-mixture HMM system. This is the first paper where we present this novel approach -called tied posteriors- for handwriting recognition. Second, we demonstrate the usage of a language model, that consists of character n-grams, as an alternative to the recognition with a large dictionary of German words. Our resulting system for character recognition yields significantly better recognition results using an unlimited vocabulary