Abstract
Signature forgery continues to create serious challenges across examinations and institutional assessments. Impersonation through falsified signatures compromises verification processes and weakens trust in recorded attendance. By applying signature verification technology supported by image processing and Artificial Intelligence, signature patterns and character structures can be examined to distinguish genuine submissions from forged ones. This digital approach supports controlled and verifiable authentication in examination environments.
Problem
Impersonation during examinations remains a persistent issue. Forged signatures allow unauthorized candidates to gain access to benefits meant for legitimate participants. Manual verification of handwritten signatures becomes increasingly difficult when handling large volumes of attendance records. Without automated participant tracking systems or biometric identification systems, institutions face limitations in validating signatures reliably and consistently.
Solution
Timing Technologies implemented an AI-based signature verification system to address impersonation during attendance validation. Signature data collected during examinations is processed using image processing techniques and deep learning models. The system compares submitted signatures with registered records to identify pattern deviations.
PyTesseract is used to extract character-level information, while Convolutional Neural Networks (CNNs) evaluate structural features of each signature. Cropped signature images are indexed and stored using registration numbers, allowing systematic comparison across records. This approach supports scalable verification without relying on manual inspection.
The signature classification model is trained using labeled signature datasets. CNN-based learning enables the system to identify variations in stroke structure, spacing, and alignment. The trained model supports attendance verification across large datasets, improving oversight in examination processes.
Benefits
Automation:Manual review is reduced through AI-driven signature comparison supported by signature verification technology
Scalability: Large volumes of attendance data can be processed using automated participant tracking systems.
Adaptability: The model can be retrained using updated signature samples as records evolve.
Transparent: Digitally recorded outputs support traceable verification decisions during audits.
Conclusion
Our signature-based detection with deep learning and CNN models provides a streamlined solution to verify attendance in examinations. Our software solution uses advanced algorithms to detect and recognize multiple signature databases, which accelerates the verification process and increases accuracy over time. With our solution, you can be confident that attendance is being verified accurately and efficiently, providing a smoother experience for both exam administrators and attendees.
