A computer simulation study of the application of contextual constraints to character recognition

Improvement of specific character recognition systems through the use of contextual constraints has been demonstrated by several researchers in the field. In this thesis, the effectiveness of using contextual constraints to improve the overall performance of a generalized character recognition system is studied experimentally. A computer simulation system designed to facilitate this investigation for both theoretical and physical character recognition system models is described. A suitable character recognition system model is developed. A number of sequential decision algorithms based on Bayes' optimum decision rule and using different order contextual constraints are implemented as the decision element of the character recognition process. The effect on the performance of the contextual decision algorithms of varying the source data, as well as varying the quality and characterization of the feature extraction system with classifier have been demonstrated. The experimental results for the practical character recognition system model confirmed the findings of others that significant improvement in overall system performance can be achieved through the application of contextual constraints. It was shown that the improvement was directly proportional to the order of contextual information used for low order statistics and that the improvement was most pronounced when these statistics were representative of the source material, the feature extraction system with classifier had a high recognition accuracy initially and its performance was characterized by mistaking the most frequently occurring characters more often than the infrequently occurring characters. It was demonstrated that (1) computer simulation is a useful tool in testing hypotheses concerning the application of contextual constraints to character recognition, (2) qualitative predictions based on theoretical character recognition system models are applicable to practical character recognition systems, and (3) the improvement which can be obtained with the application of contextual constraints to character recognition is sufficient to make machine editing of text competitive with clerical editing.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat