BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources

TL;DR

This paper provides the first comprehensive survey of Indian NLP datasets, benchmarks, and models, highlighting key trends, challenges, and opportunities for culturally grounded NLP research in India.

Contribution

It consolidates over 200 datasets, 50 benchmarks, and 100 models for Indian languages, organizing resources by linguistic features and analyzing current research trends.

Findings

Identifies data sparsity and uneven language coverage as major challenges.

Highlights the diversity of scripts and cultural contexts in Indian NLP.

Provides a structured overview to guide future equitable NLP development.

Abstract

India's linguistic landscape, spanning 22 scheduled languages and hundreds of marginalized dialects, has driven rapid growth in NLP datasets, benchmarks, and pretrained models. However, no dedicated survey consolidates resources developed specifically for Indian languages. Existing reviews either focus on a few high-resource languages or subsume Indian languages within broader multilingual settings, limiting coverage of low-resource and culturally diverse varieties. To address this gap, we present the first unified survey of Indian NLP resources, covering 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. We organize resources by linguistic phenomena, domains, and modalities; analyze trends in annotation, evaluation, and model design; and identify persistent challenges such as data sparsity, uneven language coverage, script diversity, and limited cultural and domain generalization. This survey offers a consolidated foundation for equitable, culturally grounded, and scalable NLP research in the Indian linguistic ecosystem.