Handwriting recognition with mixtures of Bayesian networks

A handwriting recognition system, comprising:

• means for encoding probabilities of observing sets of visual observations for a predetermined character;
• the means for encoding comprising plural means for modeling a hypothesis that a common hidden variable corresponding to a handwritten character associated with the means for encoding is in a respective one of a plurality of discrete states;
• each of the means for modeling comprising plural means for storing probability parameters that define relationships between visual features of the handwritten character;
• at least some of the means for storing having dependencies with others of the means for storing in each respective means for modeling;
• each means for modeling further comprising means for aggregating outputs from the plural means for storing and providing outputs of each respective means for modeling;
• and
• means for aggregating the outputs of the respective means for modeling to produce a corresponding output of each respective means for encoding indicative of the probability that an input visual observation corresponds to the handwritten character defined by the common hidden variable.

A system for training a mixture of Bayesian networks (MBN), comprising:

• the mixture of Bayesian network comprising a plurality of hypothesis-specific Bayesian networks (HSBNs), each of the HSBNs modeling a hypothesis that a common hidden variable corresponding to a handwritten character associated with the MBN is in a respective one of a plurality of discrete states;
• each of the HSBNs comprising a plurality of nodes that correspond to visual features of the handwritten character, each one of the plurality of nodes in a respective HSBN storing probability parameters that indicate how a visual feature of the one node depends on the handwritten character;
• a parameter search component that identifies a set of changes in the probability parameters that improve the goodness of each of the HSBNs in predicting visual observations of the handwritten character;
• a parameter modification component that modifies the probability parameters based on the identified set of changes;
• a scoring system that computes a structure score for each HSBN in the MBN that reflects the goodness of the each respective HSBN in predicting visual observations according to a structure of each HSBN;
• a network adjuster that searches for changes in dependencies between nodes of each HSBN that improve the structure score and modifies the dependencies so as to improve the structure score.

A method of training a mixture of Bayesian networks (MBN) to facilitate recognition of handwritten characters, the MBN encoding probabilities of observing the sets of visual observations corresponding to a handwritten character and comprising a plurality of hypothesis-specific Bayesian networks (HSBNs), each HSBN including a plurality of nodes having probability parameters with dependencies between at least some of the nodes to model a hypothesis that a common hidden variable corresponding to a handwritten character is in a respective one of a plurality of discrete states, the method comprising:

• for each one of the HSBNs:
• conducting a parameter search for a set of changes in the probability parameters which improves the goodness of the one HSBN in predicting the visual observations, and
• modifying the probability parameters of the one HSBN accordingly;
• and
• for each one of the HSBNs:
• computing a structure score of the one HSBN reflecting the goodness of the one HSBN in predicting the visual observations,
• conducting a structure search for a change in the causal links which improves the structure search score, and
• modifying the causal links of the one HSBN based on the structure search.