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Recognition and defect detection of dot-matrix text via variation-model based learning
Author(s): Wataru Ohyama; Koushi Suzuki; Tetsushi Wakabayashi
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Paper 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 %.

Paper Details

Date Published: 14 May 2017
PDF: 8 pages
Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380K (14 May 2017); doi: 10.1117/12.2264232
Show Author Affiliations
Wataru Ohyama, Mie Univ. (Japan)
Koushi Suzuki, Mie Univ. (Japan)
Tetsushi Wakabayashi, Mie Univ. (Japan)


Published in SPIE Proceedings Vol. 10338:
Thirteenth International Conference on Quality Control by Artificial Vision 2017
Hajime Nagahara; Kazunori Umeda; Atsushi Yamashita, Editor(s)

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