FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

May 27, 2019 · Entered Twilight · 🏛 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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Authors Guillaume Jaume, Hazim Kemal Ekenel, Jean-Philippe Thiran arXiv ID 1905.13538 Category cs.IR: Information Retrieval Cross-listed cs.CV, cs.LG, stat.ML Citations 462 Venue 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) Repository https://github.com/guillaumejaume/FUNSD ⭐ 5 Last Checked 7 days ago

Abstract

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. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset, which can be downloaded at https://guillaumejaume.github.io/FUNSD/.