NeuroDocs: AI Powered Document Analysis System

Abstract

The rapid digitization of documents in banking, governance, and commercial environments has significantly increased the demand for intelligent document management systems that ensure security, usability, and authenticity. In the Indian context, users routinely manage sensitive documents such as bank statements, identity proofs, certificates, and invoices, which require secure access, structured organization, multilingual understanding, and protection against fraudulent manipulation. Traditional document management systems largely focus on storage and retrieval while lacking intelligence-driven automation, regional language support, and fraud detection capabilities. This paper presents NeuroDocs, a comprehensive document analysis platform that integrates secure authentication, automated document organization, multilingual document highlighting and translation, and scam and fraud detection. The system employs OTP-based secure authentication for user access, OCR-based text extraction for document understanding, rule-based automated foldering, neural machine translation for English, Tamil, and Hindi languages, and computer vision–based seal verification for fraud detection. NeuroDocs is implemented using a React-based frontend, Node.js backend, MongoDB database, and Python-based AI services. Experimental evaluation on real-world Indian documents demonstrates improved document organization efficiency, enhanced accessibility for regional language users, and effective identification of potentially suspicious documents. The proposed system provides a practical, scalable, and secure solution for intelligent document management.

Introduction

The text presents NeuroDocs, an intelligent digital document management system designed to address security, organization, accessibility, and fraud detection challenges associated with the growing use of digital documents in India. While digitization improves efficiency and accessibility, it also introduces risks such as unauthorized access, poor document organization, language barriers, and increasing document forgery. Existing systems largely provide basic storage and retrieval, lacking integrated intelligence and security.

NeuroDocs adopts a modular, service-oriented architecture that enhances scalability, maintainability, and robustness. The system integrates multiple functionalities into a single unified framework, reducing reliance on fragmented tools. Secure access is ensured through an OTP-based email authentication mechanism, secure session management, and activity monitoring, significantly improving protection against unauthorized access.

The platform includes an automated document organization module that uses OCR and rule-based keyword analysis to categorize documents into domains such as bank, government, or others, minimizing manual effort and errors. A document highlighting and multilingual assistance module improves readability by emphasizing key information and providing selective translation and voice support in Tamil, Hindi, and English, addressing India’s linguistic diversity and improving accessibility for non-English speakers.

To combat document fraud, NeuroDocs incorporates an AI-based scam and fraud detection module that verifies the authenticity of seals and stamps using computer vision techniques, flagging suspicious documents for user attention.

The system follows a three-tier architecture consisting of a React-based presentation layer, a Node.js and Express-based application layer, and a MongoDB-powered data layer, ensuring secure, efficient processing and scalable storage. Implemented using a modular development approach, NeuroDocs provides a secure, intelligent, and user-friendly solution for managing digital documents, enhancing trust, usability, and inclusiveness while remaining adaptable to future technological advancements.

Conclusion

This paper presented NeuroDocs, an intelligent document analysis system that integrates secure authentication, automated document organization, multilingual translation, and scam and fraud detection within a unified framework. The proposed system addresses critical challenges in digital document management by combining security, accessibility, automation, and trust in a single platform tailored to the Indian context. The rule-based document organization mechanism ensures reliable and transparent categorization, while AI-driven modules provide intelligent interaction and proactive fraud awareness. The modular and scalable system architecture allows individual components to be upgraded or extended without impacting overall system stability. As a result, NeuroDocs is well suited for deployment in real-world Indian digital environments such as banking, government services, and educational institutions, where secure, intelligent, and accessible document management is essential.

References

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Copyright

Copyright © 2026 M. Krithika, U. Dhanya, F. Evanjalin Jenifer, P. Ilakkya, R. Vaishnavi, S. Yoga. 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.