Hybrid DL-ML Framework for Handwriting-Based Person Recognition

Abstract

This work proposes an efficient handwriting-person-based identification scheme amalgamating deep learning and regular machine learning classifiers. 6,955 fine-quality Arabic handwriting samples were gathered from 107 users. Deep features were extracted using Convolutional Neural Networks (CNNs), and SVM, RF, and Logistic Regression (LR) were deployed for classification. The test accuracy of feature extraction with CNN was 99.92%, while CNN+SVM ??8 reported a maximum classification accuracy of 99.95%, CNN+LR reported 98.90%, and CNN+RF exhibited 98.11%. The method has also been implemented as GUI program to facilitate user-uploaded writings-based real-time identification. Evaluation parameters such as accuracy, precision, recall, and F1-score confirm the effectiveness and robustness of the model. The findings confirm the potential of handwriting-based biometrics for secure identification and provide future directions for expanding to multilingual datasets and large-scale deployment.