The use of diverse knowledge sources in text recognition and in correction of letter substitution errors in words of text is considered. Three knowledge sources are defined: channel characteristics as probabilities that observed letters are corruptions of other letters, bottom-up context as letter conditional probabilities (when the previous letters of the word are known), and top-down context as a lexicon. Two algorithms, one based on integrating the knowledge sources in a single step and the other based on sequentially cascading bottom-up and top-down processes, are compared in terms of computational/storage requirements and results of experimentation.

A new algorithm for text recognition that corrects character substitution errors in words of text is presented.The search for a correct word effectively integrates three knowledge sources: channel characteristics, bottom-up context, and top-down context.Channel characteristics are used in the form of probabilities that observed letters are corruptions of other letters; bottom-up context is in the form of the probability of a letter when the previous letters of the word are known; and top-down context is in the form of a lexicon.A one-pass algorithm is obtained by merging a previously known dynamic programming algorithm to compute the maximum a posteriori probability string (known as the Viterbi algorithm) with searching a lexical trie.Analysis of the computational compexity of the algorithm and results of experimentation with a PASCAL implementation are presented.