# DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games

arXiv: https://arxiv.org/abs/2211.05429 · HTML: https://mlsanity.com/paper/2211.05429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
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- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [IoU Loss for 2D/3D Object Detection](https://mlsanity.com/paper/1908.03851/) (2019)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://mlsanity.com/paper/1801.02765/) (2018)
- [Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network](https://mlsanity.com/paper/1908.05900/) (2019)
- [Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction](https://mlsanity.com/paper/1811.09845/) (2018)
- [DSOD: Learning Deeply Supervised Object Detectors from Scratch](https://mlsanity.com/paper/1708.01241/) (2017)
- [SketchyScene: Richly-Annotated Scene Sketches](https://mlsanity.com/paper/1808.02473/) (2018)
- [Omnidirectional Scene Text Detection with Sequential-free Box Discretization](https://mlsanity.com/paper/1906.02371/) (2019)
- [Fast Sketch Segmentation and Labeling with Deep Learning](https://mlsanity.com/paper/1807.11847/) (2018)
- [Stroke-based sketched symbol reconstruction and segmentation](https://mlsanity.com/paper/1901.03427/) (2019)
- [SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks](https://mlsanity.com/paper/2003.00678/) (2020)
- [Scones: Towards Conversational Authoring of Sketches](https://mlsanity.com/paper/2005.07781/) (2020)
- [Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text](https://mlsanity.com/paper/2112.00800/) (2021)
- [Symbol detection in online handwritten graphics using Faster R-CNN](https://mlsanity.com/paper/1712.04833/) (2017)
- [Detecting Multi-Oriented Text with Corner-based Region Proposals](https://mlsanity.com/paper/1804.02690/) (2018)

## Abstract (arXiv)

Pictionary, the popular sketch-based guessing game, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings. However, some players occasionally draw atypical sketch content. While such content is occasionally relevant in the game context, it sometimes represents a rule violation and impairs the game experience. To address such situations in a timely and scalable manner, we introduce DrawMon, a novel distributed framework for automatic detection of atypical sketch content in concurrently occurring Pictionary game sessions. We build specialized online interfaces to collect game session data and annotate atypical sketch content, resulting in AtyPict, the first ever atypical sketch content dataset. We use AtyPict to train CanvasNet, a deep neural atypical content detection network. We utilize CanvasNet as a core component of DrawMon. Our analysis of post deployment game session data indicates DrawMon's effectiveness for scalable monitoring and atypical sketch content detection. Beyond Pictionary, our contributions also serve as a design guide for customized atypical content response systems involving shared and interactive whiteboards. Code and datasets are available at https://drawm0n.github.io.