Handwriting offers a unique behavioral biometric that can reveal critical information about a writer's identity and psychological state. This study presents a hybrid classification framework for writer identification using Hindi handwritten text, with a focus on predicting age group, gender, and anxiety level. A custom dataset was constructed containing diverse handwriting samples enriched with demographic and emotional metadata. The proposed system integrates both handcrafted features (HOG, ORB, LBP, SURF) and deep features extracted using EfficientNet, evaluated using standalone classifiers (KNN, SVM), a hybrid ensemble (SVM + KNN), and a custom Convolutional Neural Network (CNN). The hybrid models showed significant improvement over traditional classifiers, demonstrating the effectiveness of combining multiple feature representations. The CNN model outperformed all others, achieving accuracies of 83.7% for age prediction, 86.7% for gender classification, and 76.8% for anxiety estimation. These findings validate the proposed approach as a robust solution for handwriting-based personal trait identification and open new avenues for intelligent, language-specific biometric systems in real-world applications such as forensics, education, and psychological assessment.
Garg et al. (Wed,) studied this question.
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