Abstract
In this paper, we present a new dataset for Form Understanding in Noisy Scanned Documents (FUNSD). Form Understanding (FoUn) aims at extracting and structuring the textual content of forms. The dataset comprises 200 fully annotated real scanned forms. The documents are noisy and exhibit large variabilities in their representation making FoUn a challenging task. The proposed dataset can be used for various tasks including text detection, optical character recognition (OCR), spatial layout analysis and entity labeling/linking. To the best of our knowledge this is the first publicly available dataset with comprehensive annotations addressing the FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset. The FUNSD dataset can be downloaded at https://guillaumejaume.github. io/FUNSD/.
Abstract (translated)
本文提出了一种新的噪声扫描文档格式理解数据集（funsd）。形式理解（foun）旨在提取和构造形式的文本内容。数据集包括200个完全注释的真实扫描表单。这些文件噪音大，表现出很大的差异性，使其成为一项具有挑战性的任务。该数据集可用于各种任务，包括文本检测、光学字符识别（OCR）、空间布局分析和实体标记/链接。据我们所知，这是第一个公开可用的数据集，有全面的注释来解决foun任务。我们还提供了一组基线，并引入度量来评估funsd数据集的性能。funsd数据集可在https://guillaumejaume.github下载。IO/功能/
URL
https://arxiv.org/abs/1905.13538
