Deep Learning-Based Hand-Drawn Illustration in Packaging Design of Cultural and Creative Products

Abstract

Packaging design is a critical component of product marketing and branding, encompassing the visual and structural elements that encase and present goods to consumers. The hand-drawn illustration is a timeless art form that embodies the unique style, skill, and creativity of the artist's hand. This paper presents a novel approach to deep learning techniques for enhancing packaging design through the classification of hand-drawn illustrations. The proposed model is stated as a Weighted Augmented Deep Generative Network (WADGN). The proposed WADGN model uses the augmentation network for the generation of the augmented images for the creative products. With the augmented images features are extracted in the hand-drawn illustration of the products. The extracted features are implemented with the weighted augmented feature vector for the application of the generative deep learning network. The proposed WADGN model uses the feature vector of the deep learning model for the design of creative product design. With the deep learning the creative features of the hand-drawn illustration are classified for the creative package design. Simulation results demonstrated that proposed WADGN model higher performance than the conventional technique such as CNN, LSTM and SVM classifier. The proposed WADGN model achieves the ~21% higher performance than the SVM, ~16% than the LSTM and ~9% improvement than the CNN model.