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.
- 2.Namane, A., Soubari, E.H., Meyrueis, P.: Degraded dot matrix character recognition using CSM-based feature extraction. In: Proceedings of the 10th ACM Symposium on Document Engineering, pp. 207–210. ACM (2010)Google Scholar
- 3.Grafmüller, M., Beyerer, J.: Segmentation of printed gray scale dot matrix characters. In: Proceedings of 14th World Multi-conference on Systemics, Cybernetics and Informatics WMSCI, vol. 2, pp. 87–91 (2010)Google Scholar
- 4.Du, Y., Ai, H., Lao, S.: Dot text detection based on fast points. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp.435–439. IEEE (2011)Google Scholar
- 6.Ishida, H., Yanadume, S., Takahashi, T., Ide, I., Mekada, Y., Murase, H.: Recognition of low-resolution characters by a generative learning method. In: Proceedings of CBDAR, pp. 45–51 (2005)Google Scholar