<![CDATA[This research aims to develop and evaluate a Convolutional Neural Network (CNN) model for signature identification. The CNN method is chosen for its capability to extract and analyze complex visual features from signature images. The data used in this study consists of a collection of signature images divided into training and testing sets. The proposed CNN model comprises several convolutional, pooling, and fully connected layers optimized for classification tasks. Evaluation results indicate that the CNN model achieves excellent performance with an accuracy of 0.97, demonstrating high accuracy and precision in signature recognition. With these results, CNN proves to be an effective and reliable method for signature identification, making a significant contribution to the field of biometric identity verification. These findings open opportunities for further applications in security and authentication systems requiring automatic signature recognition.]]>
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