Combining bitmaps with dynamic writing information for on-line handwriting recognition

Writer independent, large vocabulary online handwriting recognition systems require robust input representations, which make optimal use of the dynamic writing information, i.e. the temporal ordering of the sampled data points. In this paper we describe an input representation for cursive handwriting, which combines this dynamic writing information with static bitmaps used in optical character recognition. This input representation is used with a connectionist recognizer, which is well suited for handling temporal sequences of patterns as provided by this kind of input representation. Our system has been tested on different cursive handwriting recognition tasks with vocabulary sizes up to 20000 words. We achieve recognition rates up to 99.5% on writer independent, single character recognition tasks and up to 98.1% on writer dependent, cursive handwriting tasks