Neural Docs: An Intelligent AI-Driven Document Management System Using OCR and Retrieval-Augmented Generation

Abstract

Managing large volumes of documents remains a challenging task for individuals and organizations, especially when data exists in unstructured formats such as scanned files, PDFs, and images. Traditional document management approaches rely heavily on manual effort for sorting, searching, and extracting information, which leads to inefficiencies and increased processing time. To address these challenges, this paper introduces Neural Docs, an AI-enabled document management system that focuses on automating document understanding and interaction. The system is designed to transform static documents into interactive and searchable knowledge sources. It utilizes Optical Character Recognition (OCR) to convert visual document content into machine-readable text. This extracted information is further processed using Natural Language Processing (NLP) techniques to identify key entities, generate metadata, and organize documents intelligently. In addition, a Retrieval-Augmented Generation (RAG) mechanism is incorporated to enable users to query documents through a conversational interface, providing responses based on relevant contextual information rather than simple keyword matching. The overall architecture is divided into multiple layers, including a user interface for interaction, a backend system for processing and coordination, and a dedicated AI module for advanced analysis. A vector-based storage mechanism is used to maintain semantic representations of documents, allowing efficient similarity-based retrieval. The system is implemented using modern technologies such as Node.js, FastAPI, and containerized deployment for flexibility and scalability. The developed solution demonstrates improved accessibility and reduced manual workload in document handling tasks. It allows users to quickly retrieve meaningful insights from stored documents and interact with them in a more intuitive way. The approach presented in this work highlights the potential of combining multiple AI techniques to create smarter and more efficient document management systems suitable for real-world applications.

Introduction

The document describes Neural Docs, an AI-powered intelligent document management system designed to overcome limitations of traditional document handling, which relies heavily on manual search, storage, and keyword-based retrieval. With the growing volume of digital documents in formats like PDFs and scanned images, existing systems struggle with efficiency, accuracy, and content understanding.

Neural Docs integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG) to convert unstructured documents into searchable, interactive, and context-aware information sources. OCR extracts text from images or scanned files, NLP identifies key metadata and structure, and RAG enables users to query documents in natural language and receive context-based answers.

The system uses a modular architecture consisting of a frontend interface, backend application layer, AI processing layer, and dual storage system (relational database + vector database). Documents are processed through a pipeline: upload → ingestion → OCR → text cleaning → NLP analysis → storage → semantic indexing → user query → RAG-based response generation.

Key features include semantic search, chatbot-based interaction, intelligent information retrieval, and scalable deployment using containerized services. The architecture separates services such as OCR, AI processing, document management, indexing, and authentication to ensure scalability and maintainability.

The expected benefits include reduced manual effort, improved document accessibility, faster search and retrieval, better decision-making, and enhanced workflow efficiency across organizations.

Conclusion

The Neural Docs system presents an integrated approach to document management by combining traditional storage mechanisms with advanced artificial intelligence techniques. The system successfully addresses the challenges associated with handling unstructured documents by automating tasks such as text extraction, metadata generation, and intelligent retrieval. As demonstrated through the implementation and results, the system is capable of converting raw documents into structured and searchable information. The use of Optical Character Recognition enables processing of scanned and image-based documents, while Natural Language Processing techniques enhance the understanding and organization of extracted content. The integration of semantic search and Retrieval-Augmented Generation further improves the system’s ability to provide context-aware responses to user queries. The inclusion of a user-friendly interface, as shown in Fig. 6.1 and Fig. 6.4, enhances accessibility and interaction, allowing users to perform document-related tasks efficiently. The system architecture and data flow, illustrated in Fig. 4.1 and Fig. 4.2, demonstrate a scalable and modular design that supports efficient integration of multiple components. However, certain limitations were identified, including dependency on OCR accuracy and computational overhead associated with AI processing. These challenges highlight opportunities for future improvements, such as enhancing text extraction accuracy and optimizing system performance. In conclusion, Neural Docs provides a scalable, efficient, and intelligent solution for document management by integrating automation and AI-driven technologies. The system demonstrates significant potential for real-world applications in domains requiring efficient document handling and information retrieval, thereby contributing to the advancement of intelligent document processing systems.

References

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Copyright

Copyright © 2026 Sahil Sanjay Haryan, Maruthu Devendrar, Gupta Rahul Shitlaprasad, Tanvirali Hadginal, Prof. Nargis Shaikh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.