An Investigation of Context-dependent and Hybrid Modeling Techniques for Very Large Vocabulary On-line Cursive Handwriting Recognition

This paper addresses a very challenging topic in on-line handwriting recognition. It deals with the problem how to further improve a baseline very large vocabulary HMM-based handwriting recognition system with a vocabulary size of 200.000 German words. The use of sophisticated HMM-technology allows the construction of such a baseline system. It is however an extremely difficult task to further improve such a system, because the very large vocabulary size leads to implementation problems related to memory and search space limitations. The paper investigates some advanced modeling techniques for further improvement of such systems and demonstrates how several special effects of these techniques can be exploited in order to increase the recognition performance without enlarging the requirements for memory allocation or search space. This is mainly achieved by introducing a novel hybrid Connectionist/HMM-approach to handwriting recognition, based on the use of so-called Maximum Mutual Information Neural Networks (MMINN) which serve as neural vector quantizer for the HMM-based handwriting recognition system. The result of this investigation is one of the first available 200k writer-dependent on-line handwriting recognition systems, which can be demonstrated on a PC and has achieved recognition rates of more than 90% for several test writers.