<![CDATA[Pattern recognition of script characters is a challenge in digital image processing. This study classifies Ulu Banyuasin script using a combination of Convolutional Neural Network (CNN) and Support Vector Machine (SVM). CNN with the VGG16 architecture is utilized for feature extraction, while classification is performed using Multi-Layer Perceptron (MLP) and SVM. The dataset undergoes preprocessing to enhance data quality. Experimental results indicate that the VGG16-SVM combination achieves an accuracy of 99%, outperforming VGG16-MLP, which attains 93%. The performance of VGG16-SVM demonstrates the effectiveness of SVM in improving accuracy after CNN-based feature extraction. However, the risk of overfitting must be considered. Strategies such as data augmentation, hyperparameter tuning, and regularization can be employed to enhance model generalization. This method has proven effective in recognizing Ulu Banyuasin script and can be applied to other character recognition systems.]]>
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