Synergizing SVM Classifier with Hybrid CNN Architecture for Handwritten Recognition

The integration of Hybrid Convolutional Neural Network (CNN) architecture with a Support Vector Machine (SVM) classifier is proposed in the study as an innovative technique for Handwritten Recognition. The necessity for the research originates from existing systems' limitations in effectively understanding handwritten material due to their dependence on basic models. Existing system limitations included low accuracy, particularly in the presence of variances in writing styles and situations. The proposed system takes advantage of CNN's feature extraction and representation capabilities and SVM's discriminative power in classification. The hybrid design improves the model's capacity to grasp complicated patterns in handwritten input, overcoming existing system limitations. The results show a considerable improvement in recognition accuracy after extensive testing and analysis, confirming the effectiveness of the proposed system. The results demonstrate the system's durability in dealing with a wide range of handwritten inputs, making it a potential alternative for real-world applications requiring accuracy and flexibility.