  Document analysis and understanding models often require extensive annotated
data to be trained. However, various document-related tasks extend beyond mere
text transcription, requiring both textual content and precise bounding-box
annotations to identify different document elements. Collecting such data
becomes particularly challenging, especially in the context of invoices, where
privacy concerns add an additional layer of complexity. In this paper, we
introduce FATURA, a pivotal resource for researchers in the field of document
analysis and understanding. FATURA is a highly diverse dataset featuring
multi-layout, annotated invoice document images. Comprising $10,000$ invoices
with $50$ distinct layouts, it represents the largest openly accessible image
dataset of invoice documents known to date. We also provide comprehensive
benchmarks for various document analysis and understanding tasks and conduct
experiments under diverse training and evaluation scenarios. The dataset is
freely accessible at https://zenodo.org/record/8261508, empowering researchers
to advance the field of document analysis and understanding.
