Character (mathematics)

Economics

Set (abstract data type)

Data mining

Computer science

Natural language processing

Optical character recognition

Task (project management)

Pattern recognition (psychology)

Mathematics

Management

Image (mathematics)

Structuring

Artificial intelligence

Geometry

Information retrieval

Programming language

Finance

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.

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents