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

Wataru Ohyama; Koushi Suzuki; Tetsushi Wakabayashi

doi:10.1117/12.2264232

14 May 2017 Recognition and defect detection of dot-matrix text via variation-model based learning

Wataru Ohyama, Koushi Suzuki, Tetsushi Wakabayashi

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380K (2017) https://doi.org/10.1117/12.2264232
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

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 %.

Citation Download Citation

Wataru Ohyama, Koushi Suzuki, and Tetsushi Wakabayashi "Recognition and defect detection of dot-matrix text via variation-model based learning", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380K (14 May 2017); https://doi.org/10.1117/12.2264232

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KEYWORDS

Printing

Optical character recognition

Defect detection

Corner detection

Image segmentation

Sensors

Detection and tracking algorithms

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