Improvement of Matching and Evaluation in Handwritten Numeral Recognition Using Flexible Standard Patterns

The purpose of this study is to develop a flexible matching method for recognizing handwritten numerals based on the statistics of shapes and structures learned from learning samples. In the recognition method we reported before, there were problems in matching of the feature points and evaluation of matching. To solve them, we propose a new matching method supplementing contour orientations with convex/concave information and a new evaluation method considering the structure of strokes. With these improvements the recognition rate rose to 96.0% from the earlier figure 91.9%. We also made a recognition experiment on samples from the ETL-1 database and obtained the recognition rate 95.2%.