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Workshop on Document Intelligence Understanding

arXiv 2307.16369 ↗First posted 31 Jul 2023cs.IR cs.CVcited by 0

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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.

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References · 10

  1. 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,288
  2. 2019PubLayNet: largest dataset ever for document layout analysiscited by 349
  3. 2021ICDAR2019 Competition on Scanned Receipt OCR and Information Extractioncited by 254
  4. 2020DocBank: A Benchmark Dataset for Document Layout Analysiscited by 152
  5. 2021VisualMRC: Machine Reading Comprehension on Document Imagescited by 150
  6. 2023PDFVQA: A New Dataset for Real-World VQA on PDF Documentscited by 28
  7. 2022V-Doc : Visual questions answers with Documentscited by 19
  8. 2022Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysiscited by 17
  9. 2023Form-NLU: Dataset for the Form Natural Language Understandingcited by 14
  10. 2022PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionalscited by 2

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