A new robust approach for altered handwritten text detection

Patil, G; Shivakumara, P; Gornale, SS; Pal, U; Blumenstein, M

A new robust approach for altered handwritten text detection

Patil, G Shivakumara, P Gornale, SS Pal, U Blumenstein, M

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2022, pp. 1-25
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Patil, G
dc.contributor.author	Shivakumara, P
dc.contributor.author	Gornale, SS
dc.contributor.author	Pal, U
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2023-03-30T21:03:38Z
dc.date.available	2023-03-30T21:03:38Z
dc.date.issued	2022-01-01
dc.identifier.citation	Multimedia Tools and Applications, 2022, pp. 1-25
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/168895
dc.description.abstract	Altering handwritten documents is receiving special attention from researchers because it is useful in several sensitive crime applications such as the identification of suicide notes, fraudulent certificates, fake answer scripts, bank and property documents etc. This paper aims at developing a robust method for detecting altered text in handwritten document images in noisy and blurry environments. This study considers ten classes of handwritten text affected by multiple forgery operations with noise and blur as follows: (i) Normal-original text, (ii) Copy-paste forgery, (iii) Insertion forgery, (iv) Copy-paste and insertion forgery, (v) Noisy text, (vi) Blurred text, (vii) Copy-paste forgery with noise, (viii) Copy-paste forgery with blur, (ix) Insertion forgery with noise and (x) Insertion with blur. For ten-class classification, the proposed work explores the combination of statistical, gradient and texture features with a Bayesian classifier. The proposed approach works based on the premise that altered content in noisy and blurry handwritten documents exhibits inconsistent patterns of pixel arrangements while the original text exhibits a regular pattern of pixel arrangements. Comprehensive experiments on our dataset of 10-class and three standard datasets, namely, a dataset of forged handwritten text, a dataset of altered receipt images, and a dataset of forged IMEI number images are conducted to show effectiveness and robustness of the proposed approach compared to the state-of-the-art methods.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-022-14242-8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	Software Engineering
dc.title	A new robust approach for altered handwritten text detection
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-30T21:03:36Z
pubs.publication-status	Published

Abstract:

Altering handwritten documents is receiving special attention from researchers because it is useful in several sensitive crime applications such as the identification of suicide notes, fraudulent certificates, fake answer scripts, bank and property documents etc. This paper aims at developing a robust method for detecting altered text in handwritten document images in noisy and blurry environments. This study considers ten classes of handwritten text affected by multiple forgery operations with noise and blur as follows: (i) Normal-original text, (ii) Copy-paste forgery, (iii) Insertion forgery, (iv) Copy-paste and insertion forgery, (v) Noisy text, (vi) Blurred text, (vii) Copy-paste forgery with noise, (viii) Copy-paste forgery with blur, (ix) Insertion forgery with noise and (x) Insertion with blur. For ten-class classification, the proposed work explores the combination of statistical, gradient and texture features with a Bayesian classifier. The proposed approach works based on the premise that altered content in noisy and blurry handwritten documents exhibits inconsistent patterns of pixel arrangements while the original text exhibits a regular pattern of pixel arrangements. Comprehensive experiments on our dataset of 10-class and three standard datasets, namely, a dataset of forged handwritten text, a dataset of altered receipt images, and a dataset of forged IMEI number images are conducted to show effectiveness and robustness of the proposed approach compared to the state-of-the-art methods.

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http://hdl.handle.net/10453/168895