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

  • Koji Endo
  • Wataru Ohyama
  • Tetsushi Wakabayashi
  • Fumitaka Kimura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9009)


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.


Training Dataset Recognition Accuracy Character Class Appearance Variation Directional Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



A part of this research is supported by OMRON Corporation.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Koji Endo
    • 1
  • Wataru Ohyama
    • 1
  • Tetsushi Wakabayashi
    • 1
  • Fumitaka Kimura
    • 1
  1. 1.Graduate School of EngineeringMie UniversityTsu-shiJapan

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