Research on hand sketch recognition method based on Sketch-AlexNet

Abstract

With the advancement of artificial intelligence and image processing technology, hand-drawn sketches can be utilized in diverse applications, such as digital creativity, design, and education, among others, highlighting their vast potential in various fields. However, existing challenges in hand-sketch recognition such as low accuracy and efficiency necessitate the development of an improved recognition method. To address these challenges, a sketch recognition method based on Sketch-AlexNet is proposed. Specifically, a larger first layer convolutional kernel is selected to enhance feature extraction ability, while a smaller step size is used to minimize information loss. Moreover, a set of multiplicative convolutional kernels is introduced to replace the original convolutional structure, enabling the network to extract features across shallow to deep hierarchies. The Sketch-AlexNet model is trained on a self-built hand-drawn sketch dataset, yielding a 9.8% accuracy improvement, better recognition results, and corresponding advancements in recognition speed and stability.