Empirical Evaluation of Fluid Trademark Similarity Identification using Enhanced Image Analysis with Deep Learning Principle

Trademark similarity identification is a critical task in intellectual property management, ensuring that newly proposed trademarks do not infringe upon existing ones. Traditional methods rely heavily on manual analysis and basic image processing techniques, which can be time-consuming and prone to errors. With the introduction of deep learning, more advanced models have been developed to automate and improve the accuracy of this process. This study presents a robust methodology for trademark similarity identification using an integrated model architecture that combines Convolutional Neural Networks (CNNs) with a pre-trained ResNet50 model. The approach leverages the deep feature extraction capabilities of ResNet50, enhanced by custom CNN layers, to capture both global and complex details of trademark images. Extensive preprocessing techniques, including image resizing, noise reduction, and edge detection, ensure high-quality inputs for the model. The proposed model achieves an accuracy of 95.7%, demonstrating superior performance compared to traditional methods. The study highlights the effectiveness of this hybrid model in accurately identifying and evaluating trademark similarities across various scenarios.