Universal Intelligent Document Management System Using OCR, Retrieval-Augmented Generation, and Large Language Models

Abstract The rapid growth of digital information has significantly increased the need for intelligent systems capable of managing, understanding, and retrieving knowledge from diverse document formats. Traditional document management systems primarily focus on storage and indexing, requiring users to manually search for relevant information, which becomes inefficient when handling large collections of scanned documents, images, and PDFs. This paper presents Universal Doc AI, a multimodal intelligent document management system that integrates Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), vector-based semantic search, and Large Language Models (LLMs) to enable context-aware question answering over user-uploaded documents and images. The proposed framework automatically extracts textual content from scanned documents and images using OCR, preprocesses and segments the extracted information into semantic chunks, and generates dense vector embeddings for efficient indexing within a vector database. When users submit natural language queries, the system retrieves the most relevant document segments through semantic similarity search and augments the retrieved context before passing it to an LLM to generate accurate, context-grounded responses. This RAG-based architecture minimizes hallucinations by restricting responses to the knowledge contained within the uploaded documents while supporting conversational interactions and multilingual document understanding. Unlike conventional document management solutions, Universal Doc AI combines secure document organization, intelligent semantic retrieval, OCR-driven knowledge extraction, and conversational AI into a unified platform. The system supports multiple document formats, including PDFs, scanned images, handwritten notes, and photographs of printed documents, making it suitable for educational institutions, enterprises, healthcare organizations, legal firms, and government agencies. Experimental evaluation demonstrates improvements in document retrieval accuracy, response relevance, and user interaction efficiency compared with traditional keyword-based search methods. The proposed framework provides a scalable, extensible, and cost-effective solution for intelligent document management by leveraging modern AI technologies, thereby enhancing accessibility, knowledge discovery, and decision-making from large-scale unstructured document repositories. Keywords: Universal Document Management, Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Multimodal AI, Document Intelligence, Semantic Search, Vector Database, Natural Language Processing (NLP), Question Answering (QA), Knowledge Retrieval, Document Understanding, Artificial Intelligence (AI), Information Retrieval, Context-Aware Conversational Systems. 1. Introduction The exponential growth of digital documents across educational institutions, enterprises, healthcare organizations, legal firms, and government agencies has created an increasing demand for intelligent document management systems. Organizations routinely store information in the form of PDF files, scanned documents, handwritten notes, invoices, reports, contracts, and images. Although these documents contain valuable knowledge, locating specific information often requires manual searching, making the retrieval process time-consuming and inefficient. Traditional document management systems primarily focus on document storage, indexing, and keyword-based search, which frequently fail to understand the semantic meaning and contextual relationships within documents. Recent advances in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have significantly improved natural language understanding and human-computer interaction. Users can now ask questions in natural language instead of relying on exact keyword matching. However, standalone LLMs are limited by static training data and may generate inaccurate or fabricated responses, commonly referred to as hallucinations. These limitations reduce their reliability for applications that require factual responses derived from user-specific documents. Retrieval-Augmented Generation (RAG) addresses this challenge by retrieving relevant information from external knowledge sources before generating responses, thereby improving factual accuracy and transparency. Optical Character Recognition (OCR) has become another key technology for document intelligence by converting printed and scanned documents into machine-readable text. Modern OCR techniques enable knowledge extraction from images, scanned PDFs, and handwritten documents, allowing information that was previously inaccessible to become searchable and analyzable. When OCR is integrated with semantic retrieval and LLM-based reasoning, users can interact with uploaded documents conversationally, obtaining precise answers without manually reviewing lengthy files. Recent studies demonstrate that combining OCR with RAG substantially enhances document understanding and contextual question answering across heterogeneous document collections. Despite these technological advancements, many existing document management systems continue to depend on metadata or keyword matching and lack intelligent semantic retrieval capabilities. Furthermore, several available AI-based systems either support only textual documents or require extensive preprocessing, limiting their applicability to real-world document repositories containing scanned images, photographs, and mixed-format documents. These limitations motivate the development of a unified framework capable of managing multiple document formats while providing reliable knowledge retrieval and conversational assistance. To address these challenges, this paper proposes UniversalDocAI, an intelligent multimodal document management system that combines OCR, Retrieval-Augmented Generation (RAG), vector-based semantic search, and Large Language Models. The proposed system automatically extracts textual content from uploaded documents and images, preprocesses the extracted information into semantic chunks, generates vector embeddings for efficient indexing, and retrieves the most relevant document passages in response to user queries. The retrieved information is then supplied to an LLM to generate context-aware answers that are grounded exclusively in the uploaded documents, thereby reducing hallucinations and improving response reliability. The proposed framework supports multiple document formats, including PDFs, scanned documents, printed images, and handwritten notes. In addition to secure document storage and management, the system enables semantic search, conversational question answering, multilingual document processing, and efficient knowledge discovery through a unified interface. Such capabilities make the framework applicable to educational institutions, corporate organizations, legal offices, healthcare providers, and government departments where rapid access to document knowledge is essential. The major contributions of this work are summarized as follows: Development of a unified AI-based document management framework capable of handling both textual and image-based documents. Integration of Optical Character Recognition (OCR) for automatic extraction of textual information from scanned and image documents. Implementation of a Retrieval-Augmented Generation (RAG) pipeline with vector-based semantic retrieval for accurate context selection. Deployment of a Large Language Model to provide conversational, document-grounded question answering while minimizing hallucinations. Design of a scalable and extensible architecture suitable for enterprise-scale document repositories and intelligent knowledge management. The remainder of this paper is organized as follows. Section II reviews the existing literature on intelligent document management, OCR, Retrieval-Augmented Generation, and Large Language Models. Section III describes the proposed methodology and system architecture. Section IV presents the implementation details and experimental evaluation. Section V discusses the obtained results, and Section VI concludes the paper with future research directions. 2. Literature Review The rapid advancement of Artificial Intelligence (AI), Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) has transformed intelligent document processing and knowledge retrieval. Several researchers have proposed AI-assisted document understanding systems; however, existing approaches continue to face challenges in handling heterogeneous document formats, multimodal content, retrieval accuracy, and hallucination-free response generation. Lewis et al. [1] introduced the Retrieval-Augmented Generation (RAG) framework, which combines neural retrieval with sequence generation to improve the factual correctness of language models. The proposed architecture demonstrated significant improvements in open-domain question answering by retrieving relevant external knowledge before generating responses. Although the framework reduced hallucinations and improved answer reliability, it primarily focused on textual knowledge sources and did not address document management, OCR-based document extraction, or multimodal document processing. Karakurt and Akbulut [2] presented a systematic literature review on integrating Retrieval-Augmented Generation with Large Language Models for enterprise knowledge management and document automation. Their study analyzed existing RAG architectures, retrieval techniques, embedding models, evaluation metrics, and enterprise applications. The review highlighted that RAG significantly enhances document automation by grounding responses in enterprise knowledge bases. However, the authors also identified several open research challenges, including effici