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@article{169351,
        author = {Ayaan Shaikh and Joshua Angre and Yash Tapse and Tanay Soni},
        title = {Leveraging OpenCV and Tensorflow in an Automated E-KYC System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1415-1420},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169351},
        abstract = {The increasing demand for secure and efficient identity verification in financial services has led to the rise of automated Electronic Know Your Customer (e-KYC) systems. This paper presents a comprehensive analysis of leveraging OpenCV and TensorFlow to enhance the e-KYC process by integrating real-time video-based human verification, natural language processing (NLP), optical character recognition (OCR), and biometric data analysis. The proposed framework utilizes real-time eye and hand movement tracking for human verification, followed by ID card detection, signature verification, and data extraction through OCR. By incorporating adaptive machine learning models, the system ensures continual improvements in accuracy and processing speed, enabling quicker customer onboarding and document verification. Additionally, the framework provides real-time feedback for discrepancies in user data, ensuring compliance with regulatory standards while enhancing user experience. This survey evaluates current methodologies and highlights the transformative potential of deep learning architectures in creating robust, user-friendly, and scalable e-KYC solutions. We conclude by outlining future research directions aimed at improving the scalability, security, and performance of automated e-KYC systems in diverse financial contexts, contributing to operational efficiency in financial institutions.},
        keywords = {},
        month = {November},
        }