# SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks

## TL;DR

SFSegNet is a deep learning model designed for automatic semantic segmentation of freehand sketches, effectively handling style variances and stroke distortions with an end-to-end approach.

## Contribution

The paper introduces SFSegNet, a novel deep FCN with affine transform encoders, specifically tailored for freehand sketch segmentation, trained on a large annotated dataset.

## Key findings

- Outperforms state-of-the-art segmentation networks
- Handles large variances in sketch styles and distortions
- Achieves high accuracy on a 10,000 sketch dataset

## Abstract

Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different drawing styles and skills; (ii) distorting lines drawn on the touchpad make sketches more difficult to be recognized; (iii) the high-performance image segmentation via deep learning technologies needs enormous annotated sketch datasets during the training stage. In this paper, we propose a Sketch-target deep FCN Segmentation Network(SFSegNet) for automatic free-hand sketch segmentation, labeling each sketch in a single object with multiple parts. SFSegNet has an end-to-end network process between the input sketches and the segmentation results, composed of 2 parts: (i) a modified deep Fully Convolutional Network(FCN) using a reweighting strategy to ignore background pixels and classify which part each pixel belongs to; (ii) affine transform encoders that attempt to canonicalize the shaking strokes. We train our network with the dataset that consists of 10,000 annotated sketches, to find an extensively applicable model to segment stokes semantically in one ground truth. Extensive experiments are carried out and segmentation results show that our method outperforms other state-of-the-art networks.