A Novel Sketch Recognition Model based on Convolutional Neural Networks

Deep neural networks have been widely used for visual recognition tasks based on real images as they have proven their efficiency. Unlike real images, sketches exhibit a high level of abstraction as they lack the rich features that the real images contain such as various colors, backgrounds, and environmental detail. Despite all of these shortages and being drawn with just a few strokes, they are still meaningful enough to encompass an appropriate level of meaning. The efficiency of deep neural networks on sketch recognition has been relatively less studied compared to the visual recognition of real images. To experiment with the efficiency of deep neural networks on sketch recognition, a novel sketch recognition model based on Convolutional Neural Networks is proposed in this study. The proposed model consisted of 21 layers and was tuned in an automated manner to find out the best-optimized model. In order to reveal the proposed model's efficiency in terms of predicting the classes of the given sketches, the model was evaluated on a gold standard sketch dataset, namely, Quick, Draw!. According to the experimental result, the proposed model's accuracy was calculated as high as 89.53% which outperformed the related work on the same dataset. The key findings that were obtained during the conducted experiments were discussed to shed light on future studies.