Summary of U-diads-bib: a Full and Few-shot Pixel-precise Dataset For Document Layout Analysis Of Ancient Manuscripts, by Silvia Zottin et al.
U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts
by Silvia Zottin, Axel De Nardin, Emanuela Colombi, Claudio Piciarelli, Filippo Pavan, Gian Luca Foresti
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces U-DIADS-Bib, a novel dataset for document layout analysis (DLA), a crucial task for both computer scientists and humanities scholars. Current datasets often prioritize computer science needs over humanities requirements, making them unsuitable for research in the humanities. To address this gap, the authors collaborate with experts from both fields to develop a pixel-precise, non-overlapping, and noiseless dataset. The proposed segmentation pipeline reduces manual annotation time, enabling ground truth generation. Additionally, a few-shot version of the dataset (U-DIADS-BibFS) is introduced, allowing for model development that can accurately analyze documents with limited sample sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to understand and study old documents. It’s hard work to look at a document page and figure out what different parts mean. Right now, there are not many good datasets to help computers do this task better. The authors of this paper created a new dataset called U-DIADS-Bib that is very precise and accurate. They also made a way for computers to help with the time-consuming job of labeling what’s in each part of the document. This will make it easier for researchers to study old documents and learn more about history. |
Keywords
* Artificial intelligence * Few shot
