Writing style detection by statistical combination of classifiers in form reader applications

The authors deal with the recognition of writing style (whether a data field is hand or machine printed) in the context of form reading applications. Due to the form reader´s hardware restrictions, the approach had to be based only on the knowledge of the surrounding rectangles of the black connected components of the data field. Different statistical classifiers were developed which were adapted to different feature vectors calculated separately for each data field. The output of these classifiers was combined, allowing a much higher performance than each single classifier. The combination was carried out by another polynomial (statistical) classifier using the estimations, not decisions, of these classifiers as the new feature vector. The improvement by combination was significant. Meanwhile the approach has proven its practical viability while running successfully in commercially distributed form readers