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“ DocVerify: Automated Documents Verification System”

Subodh S. Mohod*, Dr. Kapil Misal

1. Subodh Mohod Master of Computer Application & Trinity Academy of Engineering, Pune
2. Kapil Misal Master of Computer Application & Trinity Academy of Engineering, Pune

Abstract - The rapid digital transformation across governmental and private sectors has underscored the need for reliable, automated document verification solutions. Traditional manual verification methods are labor-intensive, slow, and prone to human error, often failing to detect sophisticated forgery techniques. With the proliferation of identity fraud and document tampering, particularly in sensitive use cases like KYC (Know Your Customer) processes, there is an urgent demand for intelligent systems that combine AI with secure digital infrastructures. This paper introduces an Automated Document Verification System (ADVS) that leverages Python-based technologies and modern web development frameworks to streamline the authentication process for identity documents.

The core technologies employed include Optical Character Recognition (OCR) using Tesseract for text extraction, a Convolutional Neural Network (CNN) for forgery detection for data integrity verification. The backend infrastructure is built using Flask, while the frontend is developed using three interchangeable stacks: React, Angular, and Next.js, offering flexibility and modern user experiences.

Our system specifically targets government-issued IDs such as Aadhaar and PAN cards in the Indian context, which are often subject to counterfeiting. Upon uploading a document, OCR is performed to extract key fields, which are then validated against standard formats and predefined field rules.

The modular architecture of ADVS supports microservices and scalable deployment. The Flask backend provides RESTful APIs for document handling and user management, while the React frontend ensures an intuitive experience with real time status updates and document previews. SQLAlchemy with SQLite manages lightweight yet secure data storage, with provisions for scaling to enterprise-grade databases. A key feature of the

system is real-time auditing and forgery detection. Every document interaction is logged with metadata (timestamp, user ID, IP address, etc.), providing transparency and traceability. Evaluation results show over 90% accuracy in validating standard-format documents, with average processing times under three seconds. A user study reported 87% satisfaction, citing ease of use and fast performance.

Keywords: - Automated Document Verification, Optical Character Recognition (OCR), Identity Authentication, Flask Framework, React Frontend, Tesseract, KYC, React.js, OpenCV, Forgery Detection, Audit Logging, Role-Based Access Control (RBAC), Digital Identity, Document Fraud Prevention, Web Based Verification System, Computer Vision, Government ID Validation.