DAZeTD: Deep Analysis of Zones in Torn Documents

Adak, C; Sharma, P; Chanda, S

DAZeTD: Deep Analysis of Zones in Torn Documents

Adak, C

Sharma, P Chanda, S

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13639 LNCS, pp. 515-529
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770
dc.contributor.author	Sharma, P
dc.contributor.author	Chanda, S
dc.date.accessioned	2023-04-18T02:02:58Z
dc.date.available	2023-04-18T02:02:58Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13639 LNCS, pp. 515-529
dc.identifier.isbn	9783031216473
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/169891
dc.description.abstract	In a crime scene, document fragments with similar contents might lead to significant evidence. A criminalist when encounters such a scene with an enormous amount of torn document pieces, automated analysis becomes imperative in procuring potential evidence in a fast and reliable way. To analyze document fragments with similar contents, a processing module to segment the homogeneous zones based on the content type is a prerequisite. This paper proposes a deep learning-based module DAZeTD that can detect textual (printed/handwritten) and non-textual ragged zones. For classifying the content of the zones, we adopt the scheme of vision transformer; and to draw the zone boundaries, we employ outer isothetic cover. We created a dataset of 881 torn documents on which we performed rigorous experiments. We obtained an overall 87.71% mAP@0.5, which is quite promising.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-031-21648-0_35
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	DAZeTD: Deep Analysis of Zones in Torn Documents
dc.type	Conference Proceeding
utslib.citation.volume	13639 LNCS
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T02:02:47Z
pubs.publication-status	Published
pubs.volume	13639 LNCS

Abstract:

In a crime scene, document fragments with similar contents might lead to significant evidence. A criminalist when encounters such a scene with an enormous amount of torn document pieces, automated analysis becomes imperative in procuring potential evidence in a fast and reliable way. To analyze document fragments with similar contents, a processing module to segment the homogeneous zones based on the content type is a prerequisite. This paper proposes a deep learning-based module DAZeTD that can detect textual (printed/handwritten) and non-textual ragged zones. For classifying the content of the zones, we adopt the scheme of vision transformer; and to draw the zone boundaries, we employ outer isothetic cover. We created a dataset of 881 torn documents on which we performed rigorous experiments. We obtained an overall 87.71% mAP@0.5, which is quite promising.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169891