DocLayout

## Overview

DocLayout is a dataset for object detection tasks - it contains Objects annotations for 3,637 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset processed from DocLayout-YOLO Dataclass mapping CLASS_NAMES = { 0: 'QR code', 1: 'advertisement', 2: 'algorithm', 3: 'answer', 4: 'author', 5: 'barcode', 6: 'bill', 7: 'blank', 8: 'bracket', 9: 'breakout', 10: 'byline', 11: 'caption', 12: 'catalogue', 13: 'chapter title', 14: 'code', 15: 'correction', 16: 'credit', 17: 'dateline', 18: 'drop cap', 19: "editor's note", 20: 'endnote', 21: 'examinee information', 22: 'fifth-level title', 23: 'figure', 24: 'first-level… See the full description on the dataset page: https://huggingface.co/datasets/v1v1d/DocLayout-140k.

Adieee5/doc_pdf_finetune_doclayout dataset hosted on Hugging Face and contributed by the HF Datasets community

IndicDLP Dataset

IndicDLP is a large-scale, foundational dataset created to advance document layout parsing in multi-lingual and multi-domain settings. It comprises 119,842 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.

Project Page : https://indicdlp.github.io/" target="_blank" rel="noopener">IndicDLP

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.