Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis

QR (quick response) Codes are one of the most popular types of two-dimensional (2D) matrix codes currently used in a wide variety of fields. Two-dimensional matrix codes, compared to 1D bar codes, can encode significantly more data in the same area. We have compared algorithms capable of localizing multiple QR Codes in an image using typical finder patterns, which are present in three corners of a QR Code. Finally, we present a novel approach to identify perspective distortion by analyzing the direction of horizontal and vertical edges and by maximizing the standard deviation of horizontal and vertical projections of these edges. This algorithm is computationally efficient, works well for low-resolution images, and is also suited to real-time processing.

[1]  Youssef Zaz,et al.  QR Code Patterns Localization based on Hu Invariant Moments , 2017 .

[2]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[3]  Khairuddin Omar,et al.  Degraded Historical Document Binarization: A Review on Issues, Challenges, Techniques, and Future Directions , 2019, J. Imaging.

[4]  Chiou-Shann Fuh,et al.  2D Barcode Image Decoding , 2013 .

[5]  Derek Bradley,et al.  Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.

[6]  Junwei Huang,et al.  Fast detection method of quick response code based on run-length coding , 2017, IET Image Process..

[7]  Jorge Calvo-Zaragoza,et al.  A selectional auto-encoder approach for document image binarization , 2017, Pattern Recognit..

[8]  Yan-Fu Kuo,et al.  QR code detection using convolutional neural networks , 2015, 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS).

[9]  Hiroshi Okumura,et al.  An Improvement on QR Code Limit Angle Detection using Convolution Neural Network , 2019, 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE).

[10]  Oleg Starostenko,et al.  Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments , 2017, J. Electr. Comput. Eng..

[11]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[12]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[13]  Suran Kong.  QR Code Image Correction based on Corner Detection and Convex Hull Algorithm , 2013, J. Multim..

[14]  Youssef Zaz,et al.  QR Code Recognition based on Principal Components Analysis Method , 2017 .

[15]  László G. Nyúl,et al.  Improved QR Code Localization Using Boosted Cascade of Weak Classifiers , 2015, Acta Cybern..

[16]  Nina Sumiko Tomita Hirata,et al.  Fast Component-Based QR Code Detection in Arbitrarily Acquired Images , 2012, Journal of Mathematical Imaging and Vision.

[17]  Anna Fabijańska,et al.  Detection of QR-Codes in Digital Images Based on Histogram Similarity , 2015 .

[18]  Elena Pivarčiová,et al.  Comparing the impact of different cameras and image resolution to recognize the data matrix codes , 2018 .

[19]  Donald G. Bailey,et al.  Zig-Zag Based Single-Pass Connected Components Analysis , 2019, J. Imaging.