Deep Learning vs. Traditional Approaches in Handwriting Recognition: A Comprehensive Performance Analysis

Abstract

Handwriting recognition (HWR) remains a significant challenge in artificial intelligence and computer vision. Traditional methods such as Hidden Markov Models (HMM), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) achieved moderate accuracy but struggled with generalization across diverse handwriting styles. Deep learning approaches, particularly Convolutional Neural Networks (CNNs) and Transformer models, have improved the field by automating feature extraction and enhancing contextual understanding. This paper presents a comprehensive performance analysis between traditional and deep learning-based HWR systems using a CNN model trained on a large handwritten character dataset. The proposed CNN achieved a recognition accuracy of 94.21%, surpassing traditional SVM (91.0%), KNN (90.03%), and HMM (88.5%) models. The findings show that deep learning architectures provide stronger accuracy and scalability for modern optical character recognition systems, especially when dealing with the variability of handwritten text. The paper also discusses computational challenges and future directions, including lightweight models, explainable AI, and hybrid CNN-Transformer architectures.