Abstract: Techniques for recognizing discrete multi-component symbolic input from a user can be applied to, for example, handwriting or speech. The techniques can include providing a database of model input sequences, where each model input sequence corresponds to a symbol to be recognized. Input functions, for example, discrete strokes, are obtained from a user and segmented into a sequence of discrete components. Hypothesis symbol sequences are obtained by comparing the discrete components to a database of symbols to be recognized and updating hypothesis symbol sequences based on the results of the comparison and hypothesis symbol sequence history from input previously acquired in time.

Abstract: Techniques for recognizing discrete multi-component symbolic input from a user can be applied to, for example, handwriting or speech. The techniques can include providing a database of model input sequences, where each model input sequence corresponds to a symbol to be recognized. Input functions, for example, discrete strokes, are obtained from a user and segmented into a sequence of discrete components. Hypothesis symbol sequences are obtained by comparing the discrete components to a database of symbols to be recognized and updating hypothesis symbol sequences based on the results of the comparison and hypothesis symbol sequence history from input previously acquired in time.