Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance multi-image understanding through large-scale pretraining on interleaved image-text datasets. However, most Vision-Language Models (VLMs) are trained primarily on English datasets, leading to inadequate representation of Indian languages. To address this gap, we introduce the Chitrakshara dataset series, covering 11 Indian languages sourced from Common Crawl. It comprises (1) Chitrakshara-IL, a large-scale interleaved pretraining dataset with 193M images, 30B text tokens, and 50M multilingual documents, and (2) Chitrakshara-Cap, which includes 44M image-text pairs with 733M tokens. This paper details the data collection pipeline, including curation, filtering, and processing methodologies. Additionally, we present a comprehensive quality and diversity analysis to assess the dataset's representativeness across Indic languages and its potential for developing more culturally inclusive VLMs.
@article{arxiv.2603.23521,
title = {Chitrakshara: A Large Multilingual Multimodal Dataset for Indian languages},
author = {Shaharukh Khan and Ali Faraz and Abhinav Ravi and Mohd Nauman and Mohd Sarfraz and Akshat Patidar and Raja Kolla and Chandra Khatri and Shubham Agarwal},
journal= {arXiv preprint arXiv:2603.23521},
year = {2026}
}
Comments
Accepted at "CVPR 2025: Workshop Vision Language Models For All"