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; and (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 is crucial for the future development of the system. However, in order to give a complete description of the basic system architecture, some of the above mentioned issues are also addressed. The surprising result of our investigation was the fact that discrete density models led to better results than continuous models, although this is generally not the case for HMM-based speech recognition systems. With the optimized system, a 70% word recognition rate was obtained for a challenging large-vocabulary, writer-independent sentence input task.