In a neural network-based character processor, a neural network identifies characters to be included in textual material using a lexicon to improve recognition performance. Using the vector confidence level quantities associated with characters from the neural network, lexicon entries are searched using a selected number of most likely characters at character positions in the lexicon entries associated with the character positions of the neural network word. As a result of this search for a predetermined number of character positions, a candidate word list is established along with the vector quantities defining the confidence level of the character being correct. At character positions following the predetermined number of character positions, the candidate list and not the lexicon is searched, the candidate list being shortened by a limited coincidence between the most likely characters of the neural network work and the characters of the lexicon words. When the neural network word has been completely processed, then, in the absence of any candidate words, the neural network word is selected. Similarly, when the neural network word is in the candidate list, then the neural network word is selected. Finally, only when the best candidate from the candidate list has a sufficiently high total confidence level, relative to the neural network word, of being correct is the candidate list word selected. <IMAGE>
Apparatus and method for a lexical post-processor for a neural network-based character processor.