DocILE is a large 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 Source: DocILE Benchmark for Document Information Localization and Extraction Image Source : https://arxiv.org/pdf/2302.05658v1.pdf
U-DIADS-Bib is a proprietary dataset developed through the collaboration of computer scientists and humanities at the University of Udine. It is composed of 200 images, 50 for each of the 4 different manuscripts that characterize it. These handwritten books were selected in collaboration with humanist partners considering both the complexity of their layout and the presence of significant and semantically distinguishable elements. In particular, the images of the four manuscripts were collected from the digital library Gallica. All manuscripts are Latin and Syriac Bibles published between the 6th and 12th centuries A.D.