Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the N

While online handwriting recognition is an area of longstanding and ongoing research, the recent emergence of portable, pen-based computers has focused urgent attention on usable, practical solutions. We discuss a combination and improvement of classical methods to produce robust recognition of hand-printed English text for a recognizer shipping in new models of Apple Computer's The ANN character classifier required some innovative training techniques to perform its task well. The dictionaries required large word lists, a regular expression grammar (to describe special constructs such as date, time, and telephone numbers), and a means of combining all these dictionaries into a comprehensive language model. In addition, well-balanced prior probabilities had to be determined for in-dictionary and out-of-dictionary writing. Together with a maximum-likelihood search engine, these elements form the basis of the so-called "Print Recognizer," which was first shipped in NEWTON OS 2.0-based MES-SAGEPAD 120 units in December 1995 and has There is ample prior work in combining low-level classifiers with dynamic time warping, hidden Markov models, Viterbi algorithms, and other search strategies to provide integrated segmentation and recognition for writing (Tappert, Suen, and Wakahara 1990) and speech (Renals et al. 1992).