Recognition and defect detection of dot-matrix text via variation-model based learning

Wataru Ohyama, Koushi Suzuki, Tetsushi Wakabayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An algorithm for recognition and defect detection of dot-matrix text printed on products is proposed. Extraction and recognition of dot-matrix text contains several difficulties, which are not involved in standard camera-based OCR, that the appearance of dot-matrix characters is corrupted and broken by illumination, complex texture in the background and other standard characters printed on product packages. We propose a dot-matrix text extraction and recognition method which does not require any user interaction. The method employs detected location of corner points and classification score. The result of evaluation experiment using 250 images shows that recall and precision of extraction are 78.60% and 76.03%, respectively. Recognition accuracy of correctly extracted characters is 94.43%. Detecting printing defect of dot-matrix text is also important in the production scene to avoid illegal productions. We also propose a detection method for printing defect of dot-matrix characters. The method constructs a feature vector of which elements are classification scores of each character class and employs support vector machine to classify four types of printing defect. The detection accuracy of the proposed method is 96.68%.

Original languageEnglish
Title of host publicationThirteenth International Conference on Quality Control by Artificial Vision 2017
EditorsAtsushi Yamashita, Hajime Nagahara, Kazunori Umeda
PublisherSPIE
ISBN (Electronic)9781510611214
DOIs
Publication statusPublished - Jan 1 2017
Event13th International Conference on Quality Control by Artificial Vision, QCAV 2017 - Tokyo, Japan
Duration: May 14 2017May 16 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10338
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other13th International Conference on Quality Control by Artificial Vision, QCAV 2017
CountryJapan
CityTokyo
Period5/14/175/16/17

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Ohyama, W., Suzuki, K., & Wakabayashi, T. (2017). Recognition and defect detection of dot-matrix text via variation-model based learning. In A. Yamashita, H. Nagahara, & K. Umeda (Eds.), Thirteenth International Conference on Quality Control by Artificial Vision 2017 [103380D] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10338). SPIE. https://doi.org/10.1117/12.2264232