A. Lawrence Spitz

Character and phoneme recognition based on probability clustering

Prior to character or phoneme recognition, a classifier provides a respective probability list for each of a sequence of sample characters or phonemes, each probability list indicating the respective samples probability for each character or phoneme type. These probability lists are clustered in character or phoneme probability space, in which each dimension corresponds to the probability that a character or phoneme candidate is an instance of a specific character or phoneme type. For each resulting cluster, data is stored indicating its cluster ID and a probability list indicating the probability of each type at the clusters center. Then, during recognition, a probability cluster identifier compares the probability list for each candidate with the probability list for each cluster to find the nearest cluster. The cluster identifier then provides the nearest clusters cluster ID to a constraint satisfier that attempts to recognize the candidate based on rules, patterns, or a combination of rules and patterns. If necessary, the constraint satisfier uses the cluster ID to retrieve the stored probability list of the cluster to assist it in recognition.