LynnHaDo/Document-Layout-Analysis — 94 Stars

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

python 3
ultralytics
numpy
opencv-python

Installation

Clone the repo

git clone https://github.com/LynnHaDo/Document-Layout-Analysis.git

Install packages

pip install ultralytics
   pip install numpy
   pip install opencv-python

Download Doclaynet dataset and save it as
```
/doclaynet-base
```
(Optional) Download pretrained YOLOv8s weights

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Works Cited

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"

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}
    }