SqueezeNet-driven AI framework for OCR of handwritten English characters and digits

Abstract

Optical character recognition (OCR) plays a pivotal role in automated data entry, document digitization, and intelligent user interfaces, particularly as digital content continues to expand across diverse applications. Handwritten character recognition remains a challenging task due to variations in writing styles, noise, and structural complexity. This study investigates the use of artificial intelligence (AI) and machine learning (ML) strategies for classifying handwritten English characters and digits, employing the orange3 platform and a dataset of 2,728 images sourced from Kaggle, partitioned into 70% training and 30% testing sets. A SqueezeNet-based feature extraction approach was implemented to generate 1,000 discriminative features, which were subsequently used to train multiple ML classifiers, including support vector machine (SVM), decision tree (DT), and random forest (RF). Experimental evaluation revealed that the RF classifier achieved the highest accuracy of 99.3%, outperforming DT (90.3%) and SVM (89.9%). These findings highlight the effectiveness of lightweight deep learning architectures such as SqueezeNet in enhancing OCR performance, while demonstrating the robustness of ensemble learning methods in achieving near-perfect classification accuracy. The proposed framework underscores the potential of combining efficient feature extraction with ML classifiers to advance OCR systems for handwritten text recognition in real-world applications.