Recognition of Data Matrix codes in images and their applications in production processes

• Autorzy: Karrach Ladislav , Pivarčiová Elena

Identyfikatory

DOI

10.2478/mspe-2020-0023

• Języki publikacji: EN

Abstrakty

EN

Data Matrix codes can be a significant factor in increasing productivity and efficiency in production processes. An important point in deploying Data Matrix codes is their recognition and decoding. In this paper is presented a computationally efficient algorithm for locating Data Matrix codes in the images. Image areas that may contain the Data Matrix code are to be identified firstly. To identify these areas, the thresholding, connected components labelling and examining outer bounding-box of the continuous regions is used. Subsequently, to determine the boundaries of the Data Matrix code more precisely, we work with the difference of adjacent projections around the Finder Pattern. The dimensions of the Data Matrix code are determined by analyzing the local extremes around the Timing Pattern. We verified the proposed method on a testing set of synthetic and real scene images and compared it with the results of other open-source and commercial solutions. The proposed method has achieved better results than competitive commercial solutions.

Słowa kluczowe

EN

adaptive thresholding; connected component labelling; data matrix code; finder pattern; timing pattern

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2020
• Tom: nr 3 (28)
• Strony: 154--161
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Karrach Ladislav

• Technical University in Zvolen Faculty of Technology Department of Manufacturing and Automation Technology Študentská 26, 960 53 Zvolen, Slovak Republic

autor

Pivarčiová Elena

• Technical University in Zvolen Faculty of Technology Department of Manufacturing and Automation Technology Študentská 26, 960 53 Zvolen, Slovak Republic, Tel: [phone]

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).