Performance Improvement of Dot-Matrix Character Recognition by Variation Model Based Learning

This paper describes an effective learning technique for optical dot-matrix characters recognition. Automatic reading system for dot-matrix character is promising for reduction of cost and labor required for quality control of products. Although dot-matrix characters are constructed by specific dot patterns, variation of character appearance due to three-dimensional rotation of printing surface, bleeding of ink and missing parts of character is not negligible. The appearance variation causes degradation of recognition accuracy. The authors propose a technique improving accuracy and robustness of dot-matrix character recognition against such variation, using variation model based learning. The variation model based learning generates training samples containing four types of appearance variation and trains a Modified Quadratic Discriminant Function (MQDF) classifier using generated samples. The effectiveness of the proposed learning technique is empirically evaluated with a dataset which contains 38 classes (2030 character samples) captured from actual products by standard digital cameras. The recognition accuracy has been improved from 78.37% to 98.52% by introducing the variation model based learning.