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

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.

Project Page : 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.