Contextual Word Recognition Using Binary Digrams

This paper describes a special-purpose character recognition system which uses contextual information for the recognition of words from any given dictionary of words. Previous techniques that utilized context involved letter transition probabilities of digrams and trigrams. This research introduces the concept of binary digrams which overcomes some of the problems of past approaches. They can be used to extract offectively the "syntax" of the dictionary while requiring very modest amounts of storage. A computationally feasible procedure is described which allows the accuracy requirements of the character recognizer to be relaxed if it is followed by a contextual postprocessor. The modified recognition system is allowed to output several alternatives for each character, while the postprocessor selects the proper string of characters by having access to both the dictionary and the dictionary syntax. A theoretical estimate of the recognition rate is derived, and experimental results demonstrate the ability of the system to achieve low error and rejection rates.