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document-layout-analysis

Object Detection Model for Scanned Documents

https://github.com/lynnhado/document-layout-analysis

Science Score: 57.0%

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    Low similarity (13.7%) to scientific vocabulary

Keywords

document-understanding object-detection python yolov8
Last synced: 6 months ago · JSON representation ·

Repository

Object Detection Model for Scanned Documents

Basic Info
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  • Stars: 90
  • Watchers: 4
  • Forks: 14
  • Open Issues: 1
  • Releases: 0
Topics
document-understanding object-detection python yolov8
Created over 2 years ago · Last pushed 12 months ago
Metadata Files
Readme License Citation

README.md


Layout Analysis of Scanned Documents

Document Layout Analysis using YOLOv8
View Demo · Report Bug · Request Feature

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 sh git clone https://github.com/LynnHaDo/Document-Layout-Analysis.git
  2. Install packages sh 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

sh 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"

  1. Doclaynet-base dataset

sh @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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Owner

  • Name: Lynn Do
  • Login: LynnHaDo
  • Kind: user

Mount Holyoke College '26

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Document Layout Analysis Baseline Model
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Linh
    family-names: Do
    email: do24l@mtholyoke.edu
repository-code: 'https://github.com/LynnHaDo/Document-Layout-Analysis'
url: >-
  https://huggingface.co/spaces/linhdo/document-layout-analysis
abstract: >-
  Baseline Object Detection Model trained on Doclaynet
  dataset
license: MIT
commit: a44264d

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