Document transFormer

Document Transformers are a class of deep learning models designed to understand and extract information from documents, addressing limitations of traditional methods in handling complex layouts and diverse data modalities. Current research focuses on improving model architectures like the Document Image Transformer (DiT) and others, often incorporating self-supervised pre-training and techniques like layout-aware prompting to enhance performance on tasks such as information extraction, classification, and question answering. These advancements are significant for various fields, enabling more efficient processing of large document collections in areas like scientific literature analysis, historical research, and legal document processing. The development of robust and efficient document transformers promises to significantly accelerate knowledge discovery and automate information extraction from diverse document sources.