[31](historical Japanese document layouts). A spectrum of models\ntrained on these datasets are currently available in the LayoutParser model zoo\nto support diÔ¨Äerent use cases.\n', metadata={'heading': '2 Related Work\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})Using PyMuPDF‚ÄãThis is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page.from langchain.document_loaders import PyMuPDFLoaderloader = PyMuPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()data[0]    Document(page_content='LayoutParser: A UniÔ¨Åed Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (ÔøΩ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model conÔ¨Ågurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\neÔ¨Äorts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for
Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.
Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: [31](historical Japanese document layouts). A spectrum of models\ntrained on these datasets are currently available in the LayoutParser model zoo\nto support diÔ¨Äerent use cases.\n', metadata={'heading': '2 Related Work\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})Using PyMuPDF‚ÄãThis is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page.from langchain.document_loaders import PyMuPDFLoaderloader = PyMuPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()data[0]    Document(page_content='LayoutParser: A UniÔ¨Åed Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (ÔøΩ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model conÔ¨Ågurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\neÔ¨Äorts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for