ResNet18-ThunderSVM: Hybrid intelligence for handwritten digit recognition by fusing deep spatial features and high-performance classification

This study addresses the efficiency and feature extraction constraints of high-performance Support Vector Machine (SVM) implementations, specifically ThunderSVM, in handling large-scale image datasets. Traditional ThunderSVM heavily relies on manually extracted features, limiting its ability to capture complex, nuanced features critical for robust image recognition. To overcome this, we propose an optimized, hybrid deep learning and machine learning model: ResNet18-ThunderSVM. This integrated architecture utilizes ResNet18 as an automated, powerful feature extractor to overcome the complexity of manual feature engineering, followed by ThunderSVM’s efficient, GPU-accelerated classification to ensure rapid training and inference. The novelty lies in demonstrating that this unique integration optimally balances the superior representational power of deep networks with the computational efficiency of ThunderSVM, a crucial trade-off often encountered in practical deployment. Experimental results on the MNIST dataset indicate that ResNet18-ThunderSVM excels in training efficiency, inference speed, parameter quantity, and performance metrics (precision, recall, F1 score, and accuracy). Compared to both traditional ThunderSVM (using manual features) and a standalone ResNet18 classifier, this hybrid approach not only accelerates training convergence but also significantly improves model generalization and stability, offering an efficient, robust, and resource-conscious solution for complex classification tasks like handwritten digit recognition.