Document Attention Network

Document Attention Networks (DANs) are a class of deep learning models designed for end-to-end processing of document images, aiming to simultaneously perform tasks like layout analysis, handwriting recognition, and information extraction. Current research focuses on improving the speed and accuracy of DANs, particularly through architectures that leverage transformer-based language models and techniques like multi-target queries and positional encoding to parallelize the inference process. These advancements are significant for applications requiring efficient and accurate processing of large volumes of handwritten or complex documents, such as historical archives digitization and automated form processing.