1Dean Academics & HOD of Department of Artificial Intelligence and Data Science, Holy Grace Academy of Engineering, Kuruvilassery, Mala, Kerala. Email: reshmignair82@gmail.com
2Professor & HOD, Dept of Computer Science and Engineering, Rajadhani Institute of Engineering and Technology, Kerala. Email: sangeethas@rietedu.in
3Assistant Professor, Dept of Computer Science and Engineering, Rajadhani Institute of Engineering and Technology, Kerala. Email: jinurajr@rietedu.in
4Assistant Professor, Dept of Computer Science and Engineering, Rajadhani Institute of Engineering and Technology, Kerala. Email: divyags@rietedu.in
5Assistant Professor, Dept of Computer Science and Engineering, Rajadhani Institute of Engineering and Technology, Kerala. Email: jincyjesudasan@rietedu.in
In modern pharmaceutical information systems, ensuring secure access to sensitive drug-related data and digital healthcare platforms has become increasingly important due to the growing threat of automated bot attacks and unauthorized access. Traditional CAPTCHA mechanisms provide a basic level of protection; however, they often compromise usability and accessibility, particularly for users accessing pharmaceutical databases and healthcare portals. This study proposes an intelligent adaptive CAPTCHA mechanism that integrates Optical Character Recognition (OCR) and Convolutional Neural Networks (CNNs) to enhance authentication and security in pharmaceutical information systems. The proposed system employs machine learning techniques and real-time behavioral analysis to dynamically adjust CAPTCHA difficulty based on user interaction patterns and response times. By continuously monitoring user behavior, the system intelligently generates personalized challenges that improve usability for legitimate users while effectively identifying automated bots. The framework incorporates multimodal CAPTCHA formats, including text and image-based challenges, improving accessibility and adaptability across diverse user groups. Additionally, the adaptive design increases resistance to automated bot training and improves overall system robustness. Experimental results demonstrate that the proposed approach achieves higher detection accuracy, reduced false-positive rates, and improved user experience compared to traditional CAPTCHA systems. The intelligent adaptive CAPTCHA mechanism provides a secure and user-friendly solution for protecting pharmaceutical data platforms, drug information systems, and digital healthcare applications from malicious automated access.
Keywords: Self-Adaptive CAPTCHA, Machine Learning, Bot Detection, OCR, CNN, Accessibility, Cybersecurity, User Experience, Multi-Modal Challenges
How to cite this article: Nair RG, Shibu S, Raj JR, Divya GS, Jesudasan J. An Intelligent Adaptive CAPTCHA Mechanism Using OCR and CNN for Secure Pharmaceutical Information Systems. Int J Drug Deliv Technol. 2026;16(11s): 429-438. DOI: 10.25258/ijddt.16.11s.43
Source of support: Nil.
Conflict of interest: None