📄 IndicDLP:

A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing

ICDAR 2025 (ORAL)

👩‍💻 Authors:

Oikantik Nath (IIT Madras)
Sahithi Kukkala (IIIT Hyderabad)
Mitesh Khapra (IIT Madras)
Ravi Kiran Sarvadevabhatla (IIIT Hyderabad)

IndicDLP Dataset

IndicDLP is a large-scale, foundational dataset created to advance document layout parsing in multi-lingual and multi-domain settings. It comprises 119,806 document images covering 11 Indic languages and English: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu. The dataset spans 12 diverse document categories, including Novels, Textbooks, Magazines, Acts & Rules, Research Papers, Manuals, Brochures, Syllabi, Question Papers, Notices, Forms, and Newspapers.

The dataset contains 42 physical and logical layout classes. IndicDLP includes both digitally-born and scanned documents, with annotations created using Shoonya, an open-source tool built on Label Studio. The dataset is curated to support robust layout understanding across diverse scripts, domains, and document types.

IndicDLP Model Checkpoints

We provide 3 model checkpoints — YOLOv10x, DocLayout-YOLO, and RoDLA — finetuned on the IndicDLP dataset. These models are optimized for robust document layout parsing across a wide range of Indic languages and document types, and are capable of detecting all 42 region labels defined in the dataset.

These checkpoints have demonstrated strong performance on both scanned and digitally-born documents. They are ready to use for inference, serve as strong baselines for benchmarking, and can be further fine-tuned for downstream tasks such as structure extraction or semantic tagging.

🏆 Available Checkpoints

Model	mAP_[50:95]	Download File	Framework
YOLOv10x	57.7	yolov10x.pt	Ultralytics YOLOv10
DocLayout-YOLO	54.5	doclayout_yolo.pt	DocLayout-YOLO
RoDLA	53.1	rodla.pth	RoDLA

🚀 Quick Start

Download the desired checkpoint(s) from this page or using the huggingface_hub CLI. See example commands for each model below.

⚙️ YOLOv10

Environment Setup

conda create -n indicdlp python=3.12
conda activate indicdlp
pip install -r requirements.txt

Training

yolo detect train \
  data=dataset_root/data.yaml \
  model=yolov10x.yaml \
  device=0,1,2,3,4,5,6,7 \
  epochs=100 \
  imgsz=1024 \
  batch=64 \
  name=indicdlp_yolov10x \
  patience=5

Evaluation

yolo detect val \
  model=/path/to/model_weights.pt \
  data=dataset_root/data.yaml \
  split=test

Inference

yolo detect predict \
  model=/path/to/model_weights.pt \
  source=dataset_root/images/test/ \
  conf=0.2 \
  save=True

⚙️ DocLayout-YOLO

For DocLayout-YOLO setup, training, and inference instructions, please see IndicDLP GitHub repository.

⚙️ RoDLA

For RoDLA setup, training, and inference instructions, please see IndicDLP GitHub repository.

📦 Dataset

These models are finetuned on the IndicDLP dataset.
For details, annotation schema, and scripts, visit the IndicDLP project homepage.

📑 Citation

If you use these models or the dataset, please cite:

@article{yourcitation2025, 
  title={IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing}, 
  author={Oikantik Nath, Sahithi Kukkala, Mitesh Khapra, Ravi Kiran Sarvadevabhatla}, 
  booktitle={International Conference on Document Analysis and Recognition (ICDAR)}, 
  year={2025}
}

🙏 Acknowledgements

📬 Contact

For issues in running code/links not working, please reach out to Sahithi Kukkala or Oikantik Nath or mention in the ISSUES section.

For questions or collaborations, please reach out to Dr. Ravi Kiran Sarvadevabhatla.

IndicDLP
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IndicDLP-model

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