Workshop on Document Intelligence Understanding
Authors: 
Soyeon Caren Han, Yihao Ding, Siwen Luo, Josiah Poon, Hee-Guen Yoon, Zhe Huang, Paul Duuring, Eun-Jung HoldenAuthors Info & Claims
Soyeon Caren Han, Yihao Ding, Siwen Luo, Josiah Poon, Hee-Guen Yoon, Zhe Huang, Paul Duuring, Eun-Jung HoldenAuthors Info & ClaimsPages 5273 - 5276
Abstract
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 release a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the model's structural and contextual understandings 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.
References
[1]
Yihao Ding, Zhe Huang, Runlin Wang, YanHang Zhang, Xianru Chen, Yuzhong Ma, Hyunsuk Chung, and Soyeon Caren Han. 2022. V-Doc: Visual questions answers with Documents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 21492--21498.
[2]
Yihao Ding, Siqu Long, Jiabin Huang, Kaixuan Ren, Xingxiang Luo, Hyunsuk Chung, and Soyeon Caren Han. 2023 a. Form-NLU: Dataset for the Form Natural Language Understanding. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2807--2816.
[3]
Yihao Ding, Siwen Luo, Hyunsuk Chung, and Soyeon Caren Han. 2023 b. PDFVQA: A New Dataset for Real-World VQA on PDF Documents. arXiv preprint arXiv:2304.06447 (2023).
[4]
Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and CV Jawahar. 2019. Icdar2019 competition on scanned receipt ocr and information extraction. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1516--1520.
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Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. 2020. DocBank: A Benchmark Dataset for Document Layout Analysis. In Proceedings of the 28th International Conference on Computational Linguistics. 949--960.
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Siwen Luo, Yihao Ding, Siqu Long, Josiah Poon, and Soyeon Caren Han. 2022. Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis. In Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2906--2916.
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Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. 2021. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2200--2209.
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Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, and Hwalsuk Lee. 2019. CORD: a consolidated receipt dataset for post-OCR parsing. In Workshop on Document Intelligence at NeurIPS 2019.
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Ryota Tanaka, Kyosuke Nishida, and Sen Yoshida. 2021. Visualmrc: Machine reading comprehension on document images. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 13878--13888.
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Zhihao Zhang, Siwen Luo, Junyi Chen, Sijia Lai, Siqu Long, Hyunsuk Chung, and Soyeon Caren Han. 2023. PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 1152--1155.
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Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. 2019. Publaynet: largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1015--1022.
Index Terms
- Workshop on Document Intelligence Understanding
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Information & Contributors
Information
Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
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Publication History
Published: 21 October 2023
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- Short-paper
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- Google Research
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
Birmingham, United Kingdom
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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
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