/Document-Layout-Analysis

Object Detection Model for Scanned Documents

Primary LanguageJupyter NotebookMIT LicenseMIT


Layout Analysis of Scanned Documents

Document Layout Analysis using YOLOv8
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Table of Contents
  1. Updates
  2. About The Project
  3. Getting Started
  4. Works Cited
  5. Acknowledgments

Updates

In this project, I provided 1 object detection model trained on the existing YOLOv8 weights. They are uploaded in my Hugging Face Space of the project. If you feel the need to use or fine-tune the models in any parts of your work, please cite this repository. Thank you, and don't forget to give this repo a 🌟!

About The Project

Due to the lack of computational resources, I only performed the training process on the Doclaynet-base dataset which contains 6910 train images, 648 val images, 499 test images. However, the model could perform relatively well, further proving the superiority of YOLOv8 model.

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Built With

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Prerequisites

  1. python 3
  2. ultralytics
  3. numpy
  4. opencv-python

Installation

  1. Clone the repo 
    git clone https://github.com/LynnHaDo/Document-Layout-Analysis.git
  2. Install packages 
    pip install ultralytics
pip install numpy
pip install opencv-python
  3. Download Doclaynet dataset and save it as datasets/doclaynet-base
  4. (Optional) Download pretrained YOLOv8s weights

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Works Cited

  1. Ultralytics YOLOv8

    authors:
 - family-names: Jocher
   given-names: Glenn
   orcid: "https://orcid.org/0000-0001-5950-6979"
 - family-names: Chaurasia
   given-names: Ayush
   orcid: "https://orcid.org/0000-0002-7603-6750"
 - family-names: Qiu
   given-names: Jing
   orcid: "https://orcid.org/0000-0003-3783-7069"
title: "YOLO by Ultralytics"
version: 8.0.0
date-released: 2023-1-10
license: AGPL-3.0
url: "https://github.com/ultralytics/ultralytics"

  2. Doclaynet-base dataset

    @article{doclaynet2022,
 title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
 doi = {10.1145/3534678.353904},
 url = {https://doi.org/10.1145/3534678.3539043},
 author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
 year = {2022},
 isbn = {9781450393850},
 publisher = {Association for Computing Machinery},
 address = {New York, NY, USA},
 booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
 pages = {3743–3751},
 numpages = {9},
 location = {Washington DC, USA},
 series = {KDD '22}
 }

Contact

Linh Do - do24l@mtholyoke.edu/dohalinh2303@gmail.com (personal)

Project Link: https://github.com/LynnHaDo/Document-Layout-Analysis

LinkedIn: https://linkedin.com/in/Linh Do

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