mlsanityData to 7 Oct 2026

DocILE 2023 Teaser: Document Information Localization and Extraction

arXiv 2301.12394 ↗First posted 29 Jan 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

The lack of data for information extraction (IE) from semi-structured business documents is a real problem for the IE community. Publications relying on large-scale datasets use only proprietary, unpublished data due to the sensitive nature of such documents. Publicly available datasets are mostly small and domain-specific. The absence of a large-scale public dataset or benchmark hinders the reproducibility and cross-evaluation of published methods. The DocILE 2023 competition, hosted as a lab at the CLEF 2023 conference and as an ICDAR 2023 competition, will run the first major benchmark for the tasks of Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) from business documents. With thousands of annotated real documents from open sources, a hundred thousand of generated synthetic documents, and nearly a million unlabeled documents, the DocILE lab comes with the largest publicly available dataset for KILE and LIR. We are looking forward to contributions from the Computer Vision, Natural Language Processing, Information Retrieval, and other communities. The data, baselines, code and up-to-date information about the lab and competition are available at https://docile.rossum.ai/.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 29 Jan 2023.

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22 of 36 references.

References · 22

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10 more
  1. 2017CloudScan - A configuration-free invoice analysis system using recurrent neural networkscited by 33
  2. 2021ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documentscited by 29
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  5. 2019Table understanding in structured documentscited by 11
  6. 2022Business Document Information Extraction: Towards Practical Benchmarkscited by 11
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