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

Abstract

While on-line handwriting recognition is an area of longstanding and ongoing research, the recent emergence of portable, penbased 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 Newton MessagePad® and eMate®. Combining an artificial neural network (ANN), as a character classifier, with a context-driven search over segmentation and word recognition hypotheses provides an efiective recognition system. Longstanding issues relative to training, generalization, segmentation, models of context, probabilistic formalisms, etc., need to be resolved, however, to get excellent performance. We present a number of recent innovations in the application of ANNs as character classifiers for word recognition, including integrated multiple representations, normalized output error, negative training, stroke warping, frequency balancing, error emphasis, and quantized weights. User-adaptation and extension to cursive recognition pose continuing challenges.KeywordsLanguage ModelOutput UnitWord Error RateHandwriting RecognitionNegative TrainingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.