DANIEL：一种快速的文档注意力网络用于手写文档的信息提取与标注
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents
AI总结：
DANIEL是一种端到端架构，结合语言模型实现手写文档的信息提取与标注，同时支持多语言和多任务学习，性能优于现有方法。
摘要：
从手写文档中提取信息通常涉及三个不同的步骤：文档布局分析、手写文本识别和命名实体识别。最近的方法尝试将这些步骤整合到一个过程中，使用完全端到端的架构。尽管如此，这些整合方法在应用于纯文本信息提取时仍未达到语言模型的性能。在本文中，我们介绍了DANIEL（用于信息提取和标注的文档注意力网络），一种完全端到端的架构，集成了语言模型并旨在实现全面的手写文档理解。DANIEL能够对整页文档进行布局识别、手写文本识别和命名实体识别。此外，它能够同时学习多种语言、布局和任务。对于命名实体识别，可以通过输入提示指定要应用的本体。该架构采用了一种能够处理任意大小图像而无需调整大小的卷积编码器，配以基于变压器语言模型的自回归解码器。DANIEL在四个数据集上实现了具有竞争力的结果，包括在RIMES 2009和M-POPP手写文本识别以及IAM NER命名实体识别上的新状态-of-the-art性能。此外，DANIEL的速度比现有方法快得多。我们提供了源代码和训练模型的权重，网址为https://github.com/Shulk97/daniel。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
