A Comparison between Continuous and Discrete Density Hidden Markov Models for Cursive Handwriting Recognition

This paper presents the results of the comparison of continuous and discrete density Hidden Markov Models (HMMs) used for cursive handwriting recognition. For comparison, a subset of a large vocabulary (1000 word), writer-independent online handwriting recognition system for word and sentence recognition was used, which was developed at Duisburg University. This system has some unique features that are rarely found in other HMM-based character recognition systems, such as: 1) Option between discrete, continuous, or hybrid modeling of HMM probability density distributions. 2) Large vocabulary recognition based on either printed or cursive word or complete sentence input. 3) Optimized HMM topology with an unusually large number of HMM states. 4) Use of multiple label streams for coding of handwritten information. Emphasis in this paper is on the comparison between continuous and discrete density HMMs, since this is still an open question in handwriting recognition, and ..

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

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF 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.