Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval
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Abstract (arXiv)
This paper introduces Fetch-A-Set (FAS), a comprehensive benchmark tailored for legislative historical document analysis systems, addressing the challenges of large-scale document retrieval in historical contexts. The benchmark comprises a vast repository of documents dating back to the XVII century, serving both as a training resource and an evaluation benchmark for retrieval systems. It fills a critical gap in the literature by focusing on complex extractive tasks within the domain of cultural heritage. The proposed benchmark tackles the multifaceted problem of historical document analysis, including text-to-image retrieval for queries and image-to-text topic extraction from document fragments, all while accommodating varying levels of document legibility. This benchmark aims to spur advancements in the field by providing baselines and data for the development and evaluation of robust historical document retrieval systems, particularly in scenarios characterized by wide historical spectrum.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 11 Jun 2024.
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16 of 39 references.
References · 16
- 2017Attention Is All You Needcited by 59,932
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2017Mask R-CNNcited by 7,259
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2020GLU Variants Improve Transformercited by 1,724
- 2019Root Mean Square Layer Normalizationcited by 1,425
- 2020On Layer Normalization in the Transformer Architecturecited by 906
- 2019FUNSD: A Dataset for Form Understanding in Noisy Scanned Documentscited by 345
4 more
- 2020A Metric Learning Reality Checkcited by 317
- 2022A Survey of Historical Document Image Datasetscited by 18
- 2018HWNet v2: An Efficient Word Image Representation for Handwritten Documentscited by 13
- 2019ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documentscited by 12
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Citing papers per year: 2026: 2.
2 papers.