Synaptics Corporation

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John C Platt

Chung-Ning Wang

Steven J Nowlan

Joseph Decker

Yi-Hsun E Cheng

Recognizing handwritten words at an electronic device. A plurality of strokes is received at a common input region of an electronic device. The plurality of strokes in combination defines a word comprising a plurality of symbols, a relative geometry of a first subset of the plurality of strokes defines a first symbol and a relative geometry of a second subset of the plurality of strokes defines a second symbol such that the relative geometry of the first subset of the plurality of strokes is not related to the relative geometry of the second subset of the plurality of strokes, and at least one stroke of the first subset of the plurality of strokes is spatially superimposed over at least one stroke of the second subset of the plurality of strokes. The word is determined using a processor of the electronic device based on the plurality of strokes without requiring recognition of the plurality of symbols, wherein a word is determined based at least in part on an entry sequence of subsets of the plurality of strokes.

Recognizing handwritten words

A method for incremental recognition of ideographic handwriting comprises in order the steps of: (1) entering in a natural stroke order at least one stroke of an ideographic character from a computer entry tablet; (2) providing the at least one stroke to an incremental character recognizer, which produces a hypothesis list of at least one candidate character; (3) displaying a hypothesis list of candidate characters containing the at least one stroke; (4) selecting a correct character from among the candidate characters on the hypothesis list if it a correct character appears thereon; (5) entering in natural stroke order at least one additional stroke of the ideographic character from the computer entry tablet if no candidate character is a correct character; (6) providing the additional stroke(s) to the incremental character recognizer, which produces an updated hypothesis list; (7) displaying the updated hypothesis list of candidate characters containing every stroke; (8) selecting a correct character from among the candidate characters on the updated hypothesis list if it a correct character appears thereon; and (9) repeating steps (5) through (8) until a correct character is selected from the updated hypothesis list.

Incremental ideographic character input method

A method for incremental recognition of ideographic handwriting comprises in order the steps of: (1) entering in a natural stroke order at least one stroke of an ideographic character from a coordinate entry tablet; (2) providing the at least one stroke to an incremental character recognizer, which produces a hypothesis list of at least one candidate character; (3) displaying a hypothesis list of candidate characters containing the at least one stroke; (4) selecting a correct character from among the candidate characters on the hypothesis list if it a correct character appears thereon; (5) entering in natural stroke order at least one additional stroke of the ideographic character from the coordinate entry tablet if no candidate character is a correct character; (6) providing the additional stroke(s) to the incremental character recognizer, which produces an updated hypothesis list; (7) displaying the updated hypothesis list of candidate characters containing every stroke; (8) selecting a correct character from among the candidate characters on the updated hypothesis list if a correct character appears thereon; and (9) repeating steps (5) through (8) until a correct character is selected from the updated hypothesis list.

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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