A Sketch Recognition Method Based on Deep Convolutional-Recurrent Neural Network

The existing sketch recognition methods ignore the stroke order information in extracting the feature of the sketch.This paper took the advantage of the stroke order information of the sketch and proposed a sketch recognition method based on deep convolutional-recurrent neural network,which combined the deep convolutional neural network and recurrent neural network.Firstly,the proposed method extracted the strokes of the sketch in sequence and obtained an ordered sequence of subsketches with increasing number of strokes.Secondly,a deep convolutional neural network was adapted to extract the feature of each subsketch in the ordered subsketch sequence and an ordered feature sequence was generated.Finally,the ordered feature sequence was input into a modified recurrent neural network,which constructed the temporal relations among the different subsketches of the same sketch to improve the accuracy of the sketch recognition.The experimental results on the largest freehand sketch dataset which is the TU-Berlin Sketch dataset show that the proposed method can effectively improve the recognition accuracy of freehand sketches.