FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

被引：179

作者：

Jaume, Guillaume ^{[1
]}

Ekenel, Hazim Kemal ^{[2
]}

Thiran, Jean-Philippe ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Signal Proc Lab 5, Lausanne, Switzerland

[2] Istanbul Tech Univ, Dept Comp Engn, Istanbul, Turkey

来源：

2019 INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION WORKSHOPS (ICDARW) AND 2ND INTERNATIONAL WORKSHOP ON OPEN SERVICES AND TOOLS FOR DOCUMENT ANALYSIS (OST), VOL 2 | 2019年

关键词：

Text detection; Optical Character Recognition; Form Understanding; Spatial Layout Analysis; ALGORITHMS;

D O I：

10.1109/ICDARW.2019.10029

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. To the best of our knowledge, this is the first publicly available dataset with comprehensive annotations to address FoUn task.

引用

页码：1 / 6

页数：6

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