Fast Detection and Recognition of QR codes in High-Resolution Images

Fast Detection and Recognition of QR codes in High-Resolution Images

Paper in proceedings outside WoS and Scopus

This paper deals with detection and recognition of matrix codes, such as the QR~codes, in high-resolution images of real-world scenes.The goal is to provide a detector capable of operation in real time even on high-resolution images (several megapixels).We present an efficient algorithm for detection of possible occurrences of the codes.  This algorithm is characterized by a very low false negative rate and a reasonable false alarm rate.  The results of our algorithm are to be followed by an accurate detection/recognition algorithm. We propose to use a recent matrix code detection and recognition algorithm based on Hough transform, because it can reuse some information computed by our new pre-detection algorithm and thus a further reduce of computational demands can be achieved. Since there are no publicly available annotated datasets for evaluation of this kind of algorithm, we collected a number of images and annotated them; these images will be made publicly available to allow for a proper comparison.  Our algorithm was evaluated on this dataset and the results are reported in the paper.

This paper deals with detection and recognition of matrix codes, such as the QR~codes, in high-resolution images of real-world scenes.The goal is to provide a detector capable of operation in real time even on high-resolution images (several megapixels).We present an efficient algorithm for detection of possible occurrences of the codes.  This algorithm is characterized by a very low false negative rate and a reasonable false alarm rate.  The results of our algorithm are to be followed by an accurate detection/recognition algorithm. We propose to use a recent matrix code detection and recognition algorithm based on Hough transform, because it can reuse some information computed by our new pre-detection algorithm and thus a further reduce of computational demands can be achieved. Since there are no publicly available annotated datasets for evaluation of this kind of algorithm, we collected a number of images and annotated them; these images will be made publicly available to allow for a proper comparison.  Our algorithm was evaluated on this dataset and the results are reported in the paper.

QR code detection, Histogram of Oriented Gradients

QR code detection, Histogram of Oriented Gradients

SZENTANDRÁSI, I.; HEROUT, A.; JURÁNKOVÁ, M.

2013

11.04.2012

Comenius University in Bratislava

Bratislava

978-1-4503-1977-5

Proceedings of 28th Spring conference on Computer Graphics

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