Smart Validation of Student Registration Documents Using CNNs with Hyperparameter Tuning and Data Augmentation: A Case Study in Higher Education

Efficient and accurate validation of student registration documents is critical for higher education institutions, yet traditional manual methods are increasingly inadequate for handling large-scale admissions. This study presents a novel application of Convolutional Neural Networks (CNNs) combined with advanced hyperparameter tuning and data augmentation techniques to automate the validation process. By fine-tuning hyperparameters using Grid Search and augmenting the dataset with context-specific transformations, the proposed model achieves a significant accuracy improvement. Trained on 500 document images, our approach demonstrates superior performance, achieving 97% accuracy for birth certificates, 88% for diplomas, 88% for family cards, 84% for identity cards, and 94% for exam result certificates, resulting in an overall accuracy of 91.1%. This research not only offers an innovative solution to the challenges of document validation in university admissions but also provides a scalable framework applicable across various domains. The deployment of this model in a real-time system further highlights its practical implications, setting a new benchmark in the field of educational administration.