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Abstract

A system for recognizing handwritten characters, including pre-processing apparatus for generating a set of features for each handwritten character, a neural network disposed for operating on sparse data structures of those features and generating a set of confidence values for each possible character symbol which might correspond to the handwritten character, and post-processing apparatus for adjusting those confidence values and for selecting a character symbol consistent with external knowledge about handwritten characters and the language they are written in. The pre-processing apparatus scales and re-parameterizes the handwritten strokes, encodes the scaled and re-parameterizd strokes into fuzzy membership vectors and binary pointwise data, and combines the vectors and data into a sparse data structure of features. The (nonconvolutional) neural network performs a matrix-vector multiply on the sparse data structure, using only the data for nonzero features collected in that structure, and, for a first layer of that neural network, using only successive chunks of the neural weights. The post-processing apparatus adjusts the confidence values for character symbols using a set of expert rules embodying common-sense knowledge, from which it generates a set of character probabilities for each character position; these character probabilities are combined with a Markov model of character sequence transitions and a dictionary of known words, to produce a final work output for a sequence of handwritten characters.

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