Information extraction from multi-layout invoice images using FATURA dataset

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. Invoices are critical documents in many business processes, but existing datasets for invoice analysis are limited in size and diversity, hindering the development of robust models. Current datasets do not adequately address the need for diverse layouts and comprehensive annotations, which are essential for training models capable of handling real-world variations in invoice documents. 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 provide an extensive evaluation using different information extraction methods under diverse training and evaluation scenarios, including precision, recall, and F1-score. The evaluation includes a visual-based approach using object detection for text region classification, a multi-modal strategy integrating visual and textual data for granular content comprehension, and a hybrid approach combining these methods. The dataset is freely accessible at this https://zenodo.org/record/8261508, empowering researchers to advance the field of document analysis and understanding.