In order to solve the existing problem of the sketch recognition heavily relying on the manual feature extraction which was very time-consuming, this paper proposed a method of sketch recognition based on deep learning, called Deep-Sketch. The classical deep learning models were mainly designed for natural color image recognition which failed on the sketch recognition. Deep-Sketch aimed to obtain more spatial structure information by using the large-size convolution kernel instead of the small-size convolution kernel in the first convolution layer. In addition, a shallow model was trained to obtain parameters which were used to initialize the corresponding layer parameters of the Deep-Sketch to reduce the model training time. Deep-Sketch was deepened with the convolution layers which kept the feature size to reduce the error rate. The results showed that the Deep-Sketch was superior to other state-of-the-art sketch recognition methods and achieved 69.2% accuracy on the sketch dataset including 250 classes
