The need for fast and accurate text entry on small handheld computers has led to a resurgence of interest in on-line word recognition using artificial neural networks. Classical methods have been combined and improved to produce robust recognition of hand-printed English text. The central concept of a neural net as a character classifier provides a good base for a recognition system; long-standing issues relative to training generalization, segmentation, probabilistic formalisms, etc., need to resolved, however, to get adequate performance. A number of innovations in how to use a neural net as a classifier in a word recognizer are presented: negative training, stroke warping, balancing, normalized output error, error emphasis, multiple representations, quantized weights, and integrated word segmentation all contribute to efficient and robust performance.
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