# DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents arXiv: https://arxiv.org/abs/2407.09103 ยท HTML: https://mlsanity.com/paper/2407.09103/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 66 references that are in the map. - First posted on arXiv 12 Jul 2024. ## Builds on (most cited first, 30 of 37) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018) - [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021) - [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020) - [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023) - [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019) - [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022) - [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020) - [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022) - [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021) - [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023) - [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://mlsanity.com/paper/2108.04539/) (2021) - [End-to-end Document Recognition and Understanding with Dessurt](https://mlsanity.com/paper/2203.16618/) (2022) - [Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition](https://mlsanity.com/paper/2005.13044/) (2020) - [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022) - [Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition](https://mlsanity.com/paper/1903.07377/) (2019) - [OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold](https://mlsanity.com/paper/2006.07491/) (2020) - [End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network](https://mlsanity.com/paper/2012.03868/) (2020) - [Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition](https://mlsanity.com/paper/1604.08352/) (2016) - [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016) - [Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition](https://mlsanity.com/paper/2005.07903/) (2020) - [Full Page Handwriting Recognition via Image to Sequence Extraction](https://mlsanity.com/paper/2103.06450/) (2021) - [Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network](https://mlsanity.com/paper/2012.04961/) (2020) - [SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition](https://mlsanity.com/paper/2102.08742/) (2021) - [Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes](https://mlsanity.com/paper/2110.05909/) (2021) ## Built on by (most cited first, 9 of 9) - [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025) - [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025) - [Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition](https://mlsanity.com/paper/2504.03349/) (2025) - [PILOT: A Promptable Interleaved Layout-aware OCR Transformer](https://mlsanity.com/paper/2504.03621/) (2025) - [Classifying the Unknown: In-Context Learning for Open-Vocabulary Text and Symbol Recognition](https://mlsanity.com/paper/2504.06841/) (2025) - [Few-shot Writer Adaptation via Multimodal In-Context Learning](https://mlsanity.com/paper/2603.29450/) (2026) - [A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR](https://mlsanity.com/paper/2604.00725/) (2026) - [HybriDLA: Hybrid Generation for Document Layout Analysis](https://mlsanity.com/paper/2511.19919/) (2025) - [Scaling State-Space Models from Lines to Paragraphs: An Ablation of Mamba-based OCR](https://mlsanity.com/paper/2606.23524/) (2026) ## Abstract (arXiv) 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 urlhttps://github.com/Shulk97/daniel.