  Hand-drawn objects usually consist of multiple semantically meaningful parts.
For example, a stick figure consists of a head, a torso, and pairs of legs and
arms. Efficient and accurate identification of these subparts promises to
significantly improve algorithms for stylization, deformation, morphing and
animation of 2D drawings. In this paper, we propose a neural network model that
segments symbols into stroke-level components. Our segmentation framework has
two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP)
network that identifies a component based on the feature. As the feature
extractor we utilize an encoder of a stroke-rnn, which is our newly proposed
generative Variational Auto-Encoder (VAE) model that reconstructs symbols on a
stroke by stroke basis. Experiments show that a single encoder could be reused
for segmenting multiple categories of sketched symbols with negligible effects
on segmentation accuracies. Our segmentation scores surpass existing
methodologies on an available small state of the art dataset. Moreover,
extensive evaluations on our newly annotated big dataset demonstrate that our
framework obtains significantly better accuracies as compared to baseline
models. We release the dataset to the community.
