Real-time on-line unconstrained handwriting recognition using statistical methods

USING STATISTICAL METHODS Krishna S. Nathan, Homayoon S. M. Beigi, Jayashree Subrahmonia, Gregory J. Clary and Hiroshi Maruyama Handwriting Recognition Group IBM Research, T.J. Watson Center, Yorktown Heights, NY 10598 ABSTRACT We address the problem of automatic recognition of unconstrained handwritten text. Statistical methods, such as hidden Markov models (HMMs) have been used successfully for speech recognition and they have recently been applied to the problem of handwriting recognition as well. In this paper, we will discuss a general recognition system for large vocabulary, writer independent, unconstrained handwritten text. \Unconstrained" implies that the user may write in any style e.g. printed, cursive or in any combination of styles. This is more representative of typical handwritten text where one seldom encounters purely printed or purely cursive forms. Furthermore, a key characteristic of the system described in this paper is that it performs recognition in real-time on 486 class PC platforms without the large amounts of memory required for traditional HMM based systems. We focus mainly on the writer independent task. Some initial writer dependent results are also reported. An error rate of 18.9% is achieved for a writerindependent 21,000 word vocabulary task in the absence of any language models.