|Larry Yaeger,||Brandyn Webb,||Richard Lyon|
|Apple Computer||Apple Computer|
The need for fast and accurate text entry on small, handheld computers has led to a resurgence of interest in on-line handwriting recognition. 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 Personal Digital Assistant. Combining an artificial neural network (ANN), as a character classifier, with a context-driven search over segmentation and word recognition hypotheses provides an effective recognition system. Long-standing issues relative to training, generalization, segmentation, models of context, probabilistic formalisms, etc., need to be resolved, however, to get excellent performance. We discuss the overall framework for our on-line print recognizer, including issues related to integrating recognition with character segmentation, word segmentation, and geometric context. We also present a number of recent innovations in the application of ANNs as character classifiers for word recognition, including multiple representations, normalized output error, negative training, stroke warping, frequency balancing, error emphasis, and quantized weights. Extensions to cursive writing and to more complex European languages pose continuing challenges.
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