DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents
<|reference_start|>DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents: Information extraction from handwritten documents involves traditionally three distinct steps: Document Layout Analysis, Handwritten Text Recognition, and Named Entity Recognition. Recent approaches have attempted to integrate these steps into a single process using fully end-to-end architectures. Despite this, these integrated approaches have not yet matched the performance of language models, when applied to information extraction in plain text. In this paper, we introduce DANIEL (Document Attention Network for Information Extraction and Labelling), a fully end-to-end architecture integrating a language model and designed for comprehensive handwritten document understanding. DANIEL performs layout recognition, handwriting recognition, and named entity recognition on full-page documents. Moreover, it can simultaneously learn across multiple languages, layouts, and tasks. For named entity recognition, the ontology to be applied can be specified via the input prompt. The architecture employs a convolutional encoder capable of processing images of any size without resizing, paired with an autoregressive decoder based on a transformer-based language model. DANIEL achieves competitive results on four datasets, including a new state-of-the-art performance on RIMES 2009 and M-POPP for Handwriting Text Recognition, and IAM NER for Named Entity Recognition. Furthermore, DANIEL is much faster than existing approaches.   We provide the source code and the weights of the trained models at \url{https://github.com/Shulk97/daniel}.<|reference_end|>
@article{constum2024daniel:,
    title={DANIEL: A fast Document Attention Network for Information Extraction and
  Labelling of handwritten documents},
    author={Thomas Constum, Pierrick Tranouez, Thierry Paquet},
    journal={arXiv preprint arXiv:2407.09103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09103},
    primaryClass={cs.AI}
}