the prediction performance can notably drop when models are ap-\nplied to target samples that are signiÔ¨Åcantly diÔ¨Äerent from the training dataset. As\ndocument structures and layouts vary greatly in diÔ¨Äerent domains, it is important\nto select models trained on a dataset similar to the test samples. A semantic syntax\nis used for initializing the model weights in LayoutParser , using both the dataset\nname and model name lp://<dataset-name>/<model-architecture-name> .'Using MathPix‚ÄãInspired by Daniel Gross's https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21from langchain.document_loaders import MathpixPDFLoaderloader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Using Unstructured‚Äãfrom langchain.document_loaders import UnstructuredPDFLoaderloader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Retain Elements‚ÄãUnder the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")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
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. ->: the prediction performance can notably drop when models are ap-\nplied to target samples that are signiÔ¨Åcantly diÔ¨Äerent from the training dataset. As\ndocument structures and layouts vary greatly in diÔ¨Äerent domains, it is important\nto select models trained on a dataset similar to the test samples. A semantic syntax\nis used for initializing the model weights in LayoutParser , using both the dataset\nname and model name lp://<dataset-name>/<model-architecture-name> .'Using MathPix‚ÄãInspired by Daniel Gross's https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21from langchain.document_loaders import MathpixPDFLoaderloader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Using Unstructured‚Äãfrom langchain.document_loaders import UnstructuredPDFLoaderloader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Retain Elements‚ÄãUnder the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")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