Recognition of printed small texture modules based on dictionary learning

EURASIP Journal on Image and Video Processing

Yun Q. Shi¹  Lifang Yu²  Zhenzhen Zhang²  Peng Cao²  Gang Cao³  Huawei Tian⁴ 

[1] Department of Electrical and Computer Engineering, New Jersey Institute of Technology;Department of Information Engineering, Beijing Institute of Graphic Communication;School of Computer Science, Communication University of China;School of National Security and Counter Terrorism, People’s Public Security University of China;

关键词: Dictionary learning;    Pattern recognition;    Print-and-scan process;   

DOI  :  10.1186/s13640-021-00573-3

来源: DOAJ

【 摘 要 】

Abstract Quick Response (QR) codes are designed for information storage and high-speed reading applications. To store additional information, Two-Level QR (2LQR) codes replace black modules in standard QR codes with specific texture patterns. When the 2LQR code is printed, texture patterns are blurred and their sizes are smaller than $$0.5{\mathrm{cm}}^{2}$$ 0.5 cm 2 . Recognizing small-sized blurred texture patterns is challenging. In original 2LQR literature, recognition of texture patterns is based on maximizing the correlation between print-and-scanned texture patterns and the original digital ones. When employing desktop printers with large pixel extensions and low-resolution capture devices, the recognition accuracy of texture patterns greatly reduces. To improve the recognition accuracy under this situation, our work presents a dictionary learning based scheme to recognize printed texture patterns. To our best knowledge, it is the first attempt to use dictionary learning to promote the recognition accuracy of printed texture patterns. In our scheme, dictionaries for all kinds of texture patterns are learned from print-and-scanned texture modules in the training stage. And these learned dictionaries are employed to represent each texture module in the testing stage (extracting process) to recognize their texture pattern. Experimental results show that our proposed algorithm significantly reduces the recognition error of small-sized printed texture patterns.

【 授权许可】

Unknown