A system and method for automatic interpretation of input printing out by means of new a posteriori wash apparent as a function of the mass and can be optimally trained data processing networks

摘要

A method and system for forming an interpretation of an input expression, where the input expression is expressed in a medium, the interpretation is a sequence of symbols, and each symbol is a symbol in a known symbol set. In general, the system processes an acquired input data set representative of the input expression, to form a set of segments, which are then used to specify a set of consegmentations. Each consegmentation and each possible interpretation for the input expression is represented in a data structure. The data structure is graphically representable by a graph comprising a two-dimensional array of nodes arranged in rows and columns and selectively connected by directed arcs. Each path, extending through the nodes and along the directed arcs, represents one consegmentation and one possible interpretation for the input expression. All of the consegmentations and all of the possible interpretations for the input expression are represented by the set of paths extending through the graph. For each row of nodes in the graph, a set of scores is produced for the known symbol set, using a complex of optimally trained neural information processing networks. Thereafter the system computes an a posteriori probability for one or more symbol sequence interpretations. By deriving each a posteriori probability solely through analysis of the acquired input data set, highly reliable probabilities are produced for competing interpretations for the input expression.