# Workshop on Document Intelligence Understanding

arXiv: https://arxiv.org/abs/2307.16369 · HTML: https://mlsanity.com/paper/2307.16369/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 11 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [VisualMRC: Machine Reading Comprehension on Document Images](https://mlsanity.com/paper/2101.11272/) (2021)
- [PDFVQA: A New Dataset for Real-World VQA on PDF Documents](https://mlsanity.com/paper/2304.06447/) (2023)
- [V-Doc : Visual questions answers with Documents](https://mlsanity.com/paper/2205.13724/) (2022)
- [Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis](https://mlsanity.com/paper/2208.10970/) (2022)
- [Form-NLU: Dataset for the Form Natural Language Understanding](https://mlsanity.com/paper/2304.01577/) (2023)
- [PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals](https://mlsanity.com/paper/2211.15940/) (2022)

## Abstract (arXiv)

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also released a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the structural and contextual understandings of proposed models on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.