@inproceedings{kolavi-etal-2025-nayana,
    title = "Nayana {OCR}: A Scalable Framework for Document {OCR} in Low-Resource Languages",
    author = "Kolavi, Adithya  and
      P, Samarth  and
      Jain, Vyoman",
    editor = "Nguyen, Duc",
    booktitle = "Proceedings of the 1st Workshop on Language Models for Underserved Communities (LM4UC 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.lm4uc-1.11/",
    pages = "86--103",
    ISBN = "979-8-89176-242-8",
    abstract = "We introduce Nayana, a scalable and efficient framework for adapting Vision-Language Models (VLMs) to low-resource languages. Despite significant advances, modern VLMs remain constrained by the scarcity of training data in non-English languages, limiting their global applicability. Our framework addresses this fundamental challenge through a novel layout-aware synthetic data generation pipeline combined with parameter-efficient adaptation techniques. Instead of requiring extensive manually annotated datasets, Nayana enables existing models to learn new languages effectively using purely synthetic data. Using Low-Rank Adaptation (LoRA), we demonstrate this capability across ten Indic languages: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu. Through extensive experiments in OCR tasks, we show that models can achieve strong performance in new languages without the traditional requirements of large-scale annotated datasets or extensive model modifications. Nayana{'}s success in adapting VLMs to new languages with synthetic data establishes a practical pathway for extending AI capabilities to underserved languages, particularly in scenarios where annotated data is scarce or unavailable."
}