JPLeoRX/detectron2-publaynet
Trained Detectron2 object detection models for document layout analysis based on PubLayNet dataset
This project provides pre-trained models that automatically identify and categorize different elements within research papers and articles. You can input scanned or digital research paper images, and it will output bounding boxes and labels for elements like text paragraphs, lists, figures, and tables. This is ideal for researchers, librarians, or data scientists working with large collections of academic documents.
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Use this if you need to quickly and accurately extract the structural layout of academic papers to automate tasks like data extraction, archiving, or content analysis.
Not ideal if you need to analyze highly specialized document types outside of academic papers or require very high precision for extremely nuanced layout elements.
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Language
Python
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Last pushed
Apr 16, 2023
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