Package: layoutparser
A unified toolkit for Deep Learning Based Document Image AnalysisWhat is LayoutParserLayoutParser aims to provide a wide range of tools that aims to streamline Document Image Analysis (DIA) tasks. Please check the LayoutParserdemo video(1 min) orfull talk(15 min) for details. And here are some key features:LayoutParser provides a rich repository of deep learning models for layout detection as well as a set of unified APIs for using them. For example,Perform DL layout detection in 4 lines of codeimportlayoutparseraslpmodel=lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')# image = Image.open("path/to/image")layout=model.detect(image)LayoutParser comes with a set of layout data structures with carefully designed APIs that are optimized for document image analysis tasks. For example,Selecting layout/textual elements in the left column of a pageimage_width=image.size[0]left_column=lp.Interval(0,image_width/2,axis='x')layout.filter_by(left_column,center=True)# select objects in the left columnPerforming OCR for each detected Layout Regionocr_agent=lp.TesseractAgent()forlayout_regioninlayout:image_segment=layout_region.crop(image)text=ocr_agent.detect(image_segment)Flexible APIs for visualizing the detected layoutslp.draw_box(image,layout,box_width=1,show_element_id=True,box_alpha=0.25)Loading layout data stored in json, csv, and even PDFslayout=lp.load_json("path/to/json")layout=lp.load_csv("path/to/csv")pdf_layout=lp.load_pdf("path/to/pdf")LayoutParser is also a open platform that enables the sharing of layout detection models and DIA pipelines among the community.Checkthe LayoutParser open platformSubmityour models/pipelines to LayoutParserInstallationAfter several major updates, layoutparser provides various functionalities and deep learning models from different backends. But it still easy to install layoutparser, and we designed the installation method in a way such that you can choose to install only the needed dependencies for your project:pipinstalllayoutparser# Install the base layoutparser library withpipinstall"layoutparser[layoutmodels]"# Install DL layout model toolkitpipinstall"layoutparser[ocr]"# Install OCR toolkitExtra steps are needed if you want to use Detectron2-based models. Please checkinstallation.mdfor additional details on layoutparser installation.ExamplesWe provide a series of examples for to help you start using the layout parser library:Table OCR and Results Parsing:layoutparsercan be used for conveniently OCR documents and convert the output in to structured data.Deep Layout Parsing Example: With the help of Deep Learning,layoutparsersupports the analysis very complex documents and processing of the hierarchical structure in the layouts.ContributingWe encourage you to contribute to Layout Parser! Please check out theContributing guidelinesfor guidelines about how to proceed. Join us!CitinglayoutparserIf you findlayoutparserhelpful to your work, please consider citing our tool andpaperusing the following BibTeX entry.@article{shen2021layoutparser,
  title={LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis},
  author={Shen, Zejiang and Zhang, Ruochen and Dell, Melissa and Lee, Benjamin Charles Germain and Carlson, Jacob and Li, Weining},
  journal={arXiv preprint arXiv:2103.15348},
  year={2021}
}