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A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

Khan, Mehshan Ahmed; Ahmed, Fawad; Khan, Muhammad Danial; Ahmad, Jawad; Kumar, Harish; Pitropakis, Nikolaos


Mehshan Ahmed Khan

Fawad Ahmed

Muhammad Danial Khan

Harish Kumar


This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%.

Journal Article Type Article
Acceptance Date Mar 15, 2022
Online Publication Date Mar 19, 2022
Publication Date 2022
Deposit Date Mar 28, 2022
Publicly Available Date Mar 28, 2022
Journal Electronics
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 6
Article Number 955
Keywords image hashing; fault detection; speeded up robust features (SURF); robustness and detection; maximum likelihood estimation sample consensus (MLESAC); feature matching; region of interest (ROI) extraction; ROI matching; feature extraction time; hash matchi
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