PragyaDoc: A Universal Document Intelligence Framework for Multilingual Medical Document Understanding in Low-Resource Settings
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Abstract (arXiv)
India's 22 official languages create a critical accessibility barrier: the majority of medical documentation exists exclusively in English, yet the patients who most urgently require this information - rural populations, ASHA workers, and patient families - are functionally excluded from understanding it. This paper presents PragyaDoc, a Universal Document Intelligence Framework that addresses this gap through a four-layer pipeline: a parallel ensemble OCR extraction layer, a geometric-lexical fusion layer, a deterministic domain structuring layer, and a dual-LLM medical reasoning and localization layer
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- 2022Large Language Models Encode Clinical Knowledgecited by 1,830
- 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
- 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
- 2019Character Region Awareness for Text Detectioncited by 278
- 2019Real-time Scene Text Detection with Differentiable Binarizationcited by 214
- 2023IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languagescited by 152
- 2022Scene Text Recognition with Permuted Autoregressive Sequence Modelscited by 149
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- 2026MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval
- 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
- 2024MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation
- 2023Coupling Symbolic Reasoning with Language Modeling for Efficient Longitudinal Understanding of Unstructured Electronic Medical Records
- 2025A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports