DocILE 2023 Teaser: Document Information Localization and Extraction

vStvep'an vSimsa, Milan vSulc, Maty'avs Skalick'y, Yash J. Patel, Ahmed Hamdi
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引用次数: 2

Abstract

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 Publications relying on large-scale datasets use only proprietary, unpublished data due to the sensitive nature of such documents.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/.
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DocILE 2023预告:文档信息本地化与提取
缺乏从半结构化商业文档中进行信息提取(IE)的数据是 IE 界面临的一个实际问题。由于此类文档的敏感性,依赖大规模数据集的出版物仅使用未公开的专有数据。公开可用的数据集大多规模较小,且针对特定领域。大规模公共数据集或基准的缺失阻碍了已发布方法的可重复性和交叉评估。DocILE 2023竞赛作为CLEF 2023会议的一个实验室和ICDAR 2023竞赛的一部分,将为商业文档中的关键信息定位和提取(KILE)和行项目识别(LIR)任务提供首个重要基准。DocILE 实验室拥有数以千计来自开放源的注释真实文档、十万个生成的合成文档和近百万个未标注文档,是公开可用的最大 KILE 和 LIR 数据集。我们期待来自计算机视觉、自然语言处理、信息检索和其他领域的贡献。有关实验室和竞赛的数据、基线、代码和最新信息,请访问 https://docile.rossum.ai/。
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