Data · dataset · 2014
Assessing cross-cut shredded document assembly
Listed in DataCite
This material contains the description and the link where to find the scripts and dataset used in the paper Assessing cross-cut shredded document assembly
Description
Abstract
In this paper we address the problem of quantitative evaluation of cross-cut shredded document reconstruction. We propose quantitative metrics using graph theory and classic information retrieval concepts to compare the neighborhood connectivity graph of a reassembled document shredded by a cross-cut machine against the neighborhood graph of the ground-truth. These metrics focus entirely on the proper relative positioning of the shredded pieces.
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To do so, we have shredded 12 documents containing diverse content, such as handwriting, printed text, images and photographs. We then scanned, extracted the pieces, and reassembled them into the ground-truth. This dataset is available to the readers, with the original documents, the digital representation of the shreds, and the scripts that provide the quantitative evaluation of the user’s reconstructions.
Links
Where it is published
- Repository landing page figshare.com/articles/dataset/Assessing_cross_cut_shredded_document_assembl… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.1224247 ↗
DOI / persistent id · from DataCite
Documentation and papers
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.1224247 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.1224247 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Computer and information sciences
- Inferred from text
- Artificial intelligence 72% · Image 65% · Text 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.1224247 | 12 d ago | JSON v1 |
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| concepts[modality].local:modality:image | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[modality].local:modality:text | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (75%) |
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