Deep Learning Based Models for Processing Hand-drawn Sketch Images

Abstract

Hand-drawn sketch allows humans to represent visual information in the real world. One of difficulties for computational systems to take hand-drawn sketches is coming from their various type of transformations when we want to compare them with realistic photo images, or consider drawing process. In this article we discuss about deep learning based methods which can take these various types of transformations. First we show how do convolutional neural network based models recognize or generate sketches as static raster images. After that we also discuss other researches which utilize recurrent neural networks in order to process sketches as dynamical processes. In the latter part of the discussion, we briefly introduce out work about integration of visuomotor information in robot's drawing process.