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Understanding documents with rich layouts and multi-modal components is a long-standing and practical task.
An overview of the tesseract ocr engine
Ray Smith · 2007
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Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references
Lutz Bornmann and Rüdiger Mutz · 2014
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Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension
Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Funsd: A dataset for form understanding in noisy scanned documents
Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran · 2019
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Icdar2019 competition on scanned receipt ocr and information extraction
Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and C. V. Jawahar · 2019
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Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, R. Manmatha, and C. V. Jawahar · 2020
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AxCell: Automatic extraction of results from machine learning papers
Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, and Robert Stojnic · 2020
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Layoutlm: Pre-training of text and layout for document image understanding
Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou · 2020
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DeepForm: Understand structured documents at scale., 2020
S. Svetlichnaya · 2020
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Plotqa: Reasoning over scientific plots
Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar · 2020
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Infographicvqa
Minesh Mathew, Viraj Bagal, Rubèn Pérez Tito, Dimosthenis Karatzas, Ernest Valveny, and C.V. Jawahar · 2021
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Due: End-to-end document understanding benchmark
Łukasz Borchmann, Michal Pietruszka, Tomasz Stanislawek, Dawid Jurkiewicz, Michał Turski, Karolina Szyndler, and Filip Gralinski · 2021
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LayoutLMv2: Multi-modal pre-training for visually-rich document understanding
Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, and Lidong Zhou · 2021
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Kleister: Key information extraction datasets involving long documents with complex layouts
Tomasz Stanislawek, Filip Grali’nski, Anna Wr’oblewska, Dawid Lipi’nski, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, and P. Biecek · 2021
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Visualmrc: Machine reading comprehension on document images
Ryota Tanaka, Kyosuke Nishida, and Sen Yoshida · 2021
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WebSRC: A dataset for web-based structural reading comprehension
Xingyu Chen, Zihan Zhao, Lu Chen, JiaBao Ji, Danyang Zhang, Ao Luo, Yuxuan Xiong, and Kai Yu · 2021
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ChartQA: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Xuan Long Do, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
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Towards complex document understanding by discrete reasoning
Fengbin Zhu, Wenqiang Lei, Fuli Feng, Chao Wang, Haozhou Zhang, and Tat-Seng Chua · 2022
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Layoutlmv3: Pre-training for document ai with unified text and image masking
Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei · 2022
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Ocr-free document understanding transformer
Geewook Kim, Teakgyu Hong, Moonbin Yim, JeongYeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Seunghyun Park · 2022
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SCROLLS: Standardized CompaRison over long language sequences
Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, and Omer Levy · 2022
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Otter: A multi-modal model with in-context instruction tuning, 2023
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Jingkang Yang, and Ziwei Liu · 2023
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CogVLM: Visual expert for pretrained language models, 2023
Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, Jiazheng Xu, Bin Xu, Juanzi Li, Yuxiao Dong, Ming Ding, and Jie Tang · 2023
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Hierarchical multimodal transformers for multi-page docvqa, 2023
Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny · 2023
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Document understanding dataset and evaluation (DUDE)
Jordy Van Landeghem, Rubèn Pérez Tito, Łukasz Borchmann, Michal Pietruszka, Pawel J’oziak, Rafal Powalski, Dawid Jurkiewicz, Mickaël Coustaty, Bertrand Ackaert, Ernest Valveny, Matthew B. Blaschko, Sien Moens, and Tomasz Stanislawek · 2023
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SlideVQA: A dataset for document visual question answering on multiple images
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