DAZeTD: Deep Analysis of Zones in Torn Documents
- 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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Filename | Description | Size | |||
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DAZeTD Deep Analysis of Zones in Torn Documents.pdf | Published version | 88.06 MB |
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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.
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