PubLayNet

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2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	1
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2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	1

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vivegi·Nov 15, 2023

Layout analysis is the key. Quite a bit of work has been going on recently in this area. Some papers of relevance: - Xu Zhong, Jianbin Tang, Antonio Jimeno Yepes. "PubLayNet: largest dataset ever for document layout analysis," Aug 2019. Preprint: https://arxiv.org/abs/1908.07836 Code/Data: https://github.com/ibm-aur-nlp/PubLayNet - B. Pfitzmann, C. Auer, M. Dolfi, A. S. Nassar and P. Staar, "DocLayNet: a large human-annotated dataset for document-layout analysis," 13 August 2022. [Online]. Available: https://developer.ibm.com/exchanges/data/all/doclaynet/. - S. Appalaraju, B. Jasani, B. U. Kota, Y. Xie and R. Manmatha, "Docformer: End-to-end transformer for document understanding.," in The International Conference on Computer Vision (ICCV 2021), 2021. The first one is for publications. From the abstract: "...the PubLayNet dataset

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for document layout analysis by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central. The size of the dataset is comparable to established computer vision datasets, containing over 360 thousand document images, where typical document layout elements are annotated". The second is for documents. It contains 80K manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement.

In Ask HN: Why are all OCR outputs so raw?

visarga·Sep 14, 2020

Using ML it is possible to parse PDF pages and interpret their layout and contents. Coupled with summarisation and text to speech it could make PDFs accessible to blind people. The parsing software would run on the user system and be able to cover all sorts of inputs, even images and web pages, as it is just OCR, CV and NLP. The advantage is that PDFs become accessible immediately as opposed to the day all PDF creators would agree on a better presentation. Example: A layout dataset - https://github.com/ibm-aur-nlp/PubLayNet and: SoundGlance: Briefing the Glanceable Cues of Web Pages for Screen Reader Users http://library.usc.edu.ph/ACM/CHI2019/2exabs/LBW1821.pdf A related problem is invoice/receipt/menu/form information extraction. Also, image based ad filtering.

In What's so hard about PDF text extraction?