PT Unknown
AU Stepan Simsa
   Milan Sulc
   Michal Uricar
   Yash Patel
   Ahmed Hamdi
   Matej Kocian
   Matyas Skalicky
   Jiri Matas
   Antoine Doucet
   Mickael Coustaty
   Dimosthenis Karatzas
TI DocILE Benchmark for Document Information Localization and Extraction
BT 17th International Conference on Document Analysis and Recognition
PY 2023
BP 147–166
VL 14188
DE Document AI; Information Extraction; Line Item Recognition; Business Documents; Intelligent Document Processing
AB This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly 1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: (i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin; (ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table; (iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set. The benchmark comes with several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at https://github.com/rossumai/docile.
ER