NDLI: Forged text detection in video, scene, and document images

Content Provider	IET Digital Library
Author	Nandanwar, Lokesh Shivakumara, Palaiahnakote Mondal, Prabir Raghunandan, Karpuravalli Srinivas Pal, Umapada Lu, Tong Lopresti, Daniel
Abstract	Rapid advances in artificial intelligence have made it possible to produce forgeries good enough to fool an average user. As a result, there is growing interest in developing robust methods to counter such forgeries. This study presents a new Fourier spectrum-based method for detecting forged text in video images. The authors' premise is that brightness distribution and the spectrum shape exhibit irregular patterns (inconsistencies) for forged text, while appearing more regular for original text. The method divides the spectrum of an input image into sectors and tracks to highlight these effects. Specifically, positive and negative coefficients for sectors and tracks are extracted to quantify the brightness distribution. Variations in the shape of the spectrum are analysed by determining the angular relationship between the principal axes and the sectors/tracks of the spectrum. Next, it combines these two features to detect forged text in the images of IMEI (International Mobile Equipment Identity) numbers and document. For evaluation, the following datasets are used: own video dataset and standard datasets, namely, IMEI number, ICPR 2018 Fraud Document Contest, and a natural scene text dataset. Experimental results show that the proposed method outperforms existing methods in terms of average classification rate and F-score.
Starting Page	4744
Ending Page	4755
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0590
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Journal	IET Image Processing
Publisher Date	2021-01-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Intelligence Brightness Distribution Complete Spectrum Computer Vision And Image Processing Technique Document Image Document Image Processing Feature Extraction Forged Text Detection Forgeries Fourier Spectrum-based Method Image Classification Image Colour Analysis Image Recognition Image Segmentation IMEI Numbers Input Image Irregular Pattern Learning in AI Machine Learning Method Divides Natural Scene Text Dataset Natural Scenes Original Text Robust Method Scene Image Spectrum Shape Text Analysis Text Detection Video Dataset Video Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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