Nayana OCR : A Scalable Framework for Document OCR in Low-Resource Languages (2025)

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.

## introduction vision-language models @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ) have demonstrated remarkable success in high-resource languages like english. however, these advancements have not translated across all languages due to a fundamental challenge: the scarcity of high-quality training data. this limitation is particularly evident in languages with complex scripts, where creating large-scale manually annotated datasets is both time-consuming and prohibitively expensive. this has limited the adoption of vlms for document understanding tasks across diverse languages. nayana is an adaptive framework designed to bridge this gap by enabling existing vlms to learn new languages effectively without requiring extensive annotated datasets. while this paper demonstrates nayana's capabilities through ocr tasks across ten indic languages, the framework's approach is inherently flexible and can extend to other tasks and language families. our methodology eliminates the traditional requirement of annotation by combining synthetic data generation with efficient model adaptation techniques. the main contributions of this paper are: ## synthetic data generation: a scalable cross-lingual framework the cornerstone of our work lies in developing a sophisticated pipeline for generating high-fidelity synthetic training data that preserves the intricate relationships between document layout, visual elements, and textual content across languages. our framework addresses the fundamental challenge of data scarcity in low-resource languages through a novel approach that combines advanced document understanding, a state-of-the-art english ocr model, and context-aware translation mechanisms. this section details the architectural components and methodological innovations that enable scalable, high-quality dataset generation for multilingual document understanding tasks. the pipeline's design emphasizes three critical aspects: preservation of document structure and visual hierarchy, accurate text recognition across diverse scripts, and contextually appropriate translation that maintains semantic integrity. through careful orchestration of these elements, we achieve a system capable of generating training data that closely mirrors the complexity and nuance of naturally occurring documents while scaling efficiently across multiple languages and document types. ## architectural innovation: parameter-efficient cross-script learning the adaptation of vision-language models (vlms) for multilingual document understanding presents a fundamental architectural challenge: how to effectively extend models trained primarily on latin scripts to handle dramatically different writing systems while maintaining computational efficiency. this section details our systematic exploration of architectural approaches, empirically-driven design decisions, and the development of our parameterefficient adaptation methodology. our initial investigation began with a comprehensive evaluation of contemporary vlm architectures, analyzing their fundamental capabilities in handling text-dense images. this exploration revealed a critical insight: while many models excel at general visual understanding, they often struggle with the precise geometric and spatial relationships inherent in document processing. through extensive experimentation with architectures ranging from traditional cnn-based models to state-ofthe-art transformer variants, we identified several key architectural requirements that would prove crucial for successful cross-script adaptation. ## conclusion this work establishes that vision-language models can be effectively adapted to new languages using purely synthetic data, reducing dependency on costly manual annotation. our results demonstrate that nayana provides a practical, scalable solution for extending ai capabilities to low-resource languages. by achieving strong performance across diverse scripts while maintaining computational efficiency, our framework opens new possibilities for democratizing ai technologies across linguistic boundaries. the success of our approach not only validates the effectiveness of synthetic data generation and efficient adaptation techniques but also establishes a promising direction for developing more inclusive ai systems that can serve diverse linguistic communities worldwide. 100 figure 12: page-level translation examples showing malayalam (left) and gujarati (right) translations. 101 figure 14: page-level translation examples showing odia (left) and punjabi (right) translations. 102 a.5 data augmentation examples figure 19: document degradation examples showing (from left to right): background texturization, printer drum defects, ink mottling effect, and letterpress impression.