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Quick, Draw!

Quick, Draw! is an online guessing game and artificial intelligence experiment developed by Google Creative Lab, in which players draw a prompted object or idea on a digital canvas within 20 seconds while a recurrent neural network attempts to recognize and identify the sketch in real time.^[1]
Launched in November 2016, the game serves a dual purpose as both an engaging Pictionary-style activity and a crowdsourcing tool to collect human drawings for training machine learning models on sketch recognition.^[2]^[3]
Contributions from over 15 million players have built the Quick, Draw! dataset, a publicly available collection of more than 50 million anonymized vector drawings across 345 categories such as animals, vehicles, and household items, which is utilized by researchers and developers to advance AI capabilities in image understanding and generative models.^[4]^[5]
By December 2017, the game had amassed over one billion drawings from users worldwide, highlighting its popularity and the scale of data generated for ongoing machine learning research.^[6]

Introduction and History

Overview

Quick, Draw! is an online guessing game developed by Google in which players draw objects or concepts on their screen while a neural network attempts to identify them in real-time.^[7] The game challenges participants to complete sketches quickly, fostering an interactive experience that demonstrates artificial intelligence capabilities through immediate feedback on the AI's recognition process.^[6] The core purpose of Quick, Draw! extends beyond entertainment, as it collects anonymized player drawings to contribute to a vast dataset used for training machine learning models in image recognition.^[3] By engaging users worldwide, the game has amassed millions of contributions, enabling ongoing improvements in AI's ability to interpret human sketches.^[6] Key features include a 20-second time limit per drawing to encourage rapid creation, full accessibility via web browsers without requiring downloads or installations, and post-game visualizations that illustrate the AI's learning patterns from similar drawings.^[8] Launched on November 14, 2016, as part of Google's AI Experiments initiative, Quick, Draw! has become a popular tool for both casual play and educational exploration of machine learning concepts.^[9]

Development and Release

Quick, Draw! originated from an idea conceived by Jonas Jongejan, a creative technologist at Google Creative Lab, during an internal hackathon while brainstorming projects for human-AI interaction.^[10] The game was initially developed as a demonstration of sketch-based AI recognition, leveraging machine learning to enable real-time guessing of user drawings.^[3] The project was built on Google's App Engine to ensure scalability and handle growing user participation, allowing seamless deployment and data processing for the neural network's training.^[3] Key contributors included Henry Rowley, Takashi Kawashima, Jongmin Kim, and Nick Fox-Gieg, alongside teams from Google Creative Lab and the Data Arts Team, who collaborated to integrate the drawing interface with the underlying AI model.^[1] Quick, Draw! made its full public launch on November 14, 2016, as part of Google's AI Experiments platform at aiexperiments.withgoogle.com.^[11] The release was motivated by exploring engaging ways to demonstrate machine learning while collecting anonymized drawing data to advance AI research in visual recognition.^[1] As of 2025, the core game has seen no major updates, maintaining its original mechanics and focus on crowdsourced data contribution.^[7]

Gameplay

Core Mechanics

Quick, Draw! operates through a series of interactive drawing rounds designed to test the AI's recognition capabilities while engaging players in simple sketching tasks. The game structure consists of six rounds, with each round presenting a random prompt for an object, animal, or concept, such as "cat," "airplane," or "The Mona Lisa."^[12]^[13] In each round, players draw the prompted item on a digital canvas using a mouse, trackpad, or touch input, constrained by a 20-second timer. As strokes are added, the neural network analyzes the evolving sketch in real time and displays potential guesses as animated typing text suggestions on the screen.^[14]^[15] A round resolves when the AI correctly identifies the drawing before the timer expires—resulting in a successful "win" for that round—or when the time runs out, at which point the game proceeds to the next prompt without any penalties for incomplete or inaccurate drawings.^[15]^[16] Upon completing all six rounds, the game concludes with a summary screen that recaps each of the player's drawings alongside the AI's final guesses, while also showing anonymized examples of similar drawings contributed by other users for comparative context.^[12]

User Experience

Quick, Draw! provides a minimalist and intuitive interface centered on a blank digital canvas that occupies the main screen area, allowing users to create simple line drawings in response to randomly generated prompts. As users sketch, the AI's evolving guesses appear as animated text overlays in real time, often "typing out" predictions with a dynamic effect that updates with each stroke to reflect the neural network's interpretation. This design emphasizes speed and simplicity, stripping away complex tools to focus on rapid, gestural input that simulates casual doodling.^[1]^[14] The game supports versatile input methods, including mouse or trackpad for desktop users and direct touchscreen interaction for mobile devices, enabling stroke-based drawing without colors, fills, or erasers to keep the experience unencumbered and true to quick sketches. A prominent 20-second timer visually counts down in the interface, building urgency, while success triggers celebratory visual animations, such as colorful bursts or confirmation messages, to reinforce positive outcomes. Audio feedback accompanies the guesses through synthesized speech that vocalizes the AI's predictions, enhancing immersion, though users may need to ensure browser audio permissions are enabled.^[7]^[3]^[15] Accessibility is prioritized through broad browser compatibility, with optimal performance in Google Chrome, and a fully responsive design that adapts seamlessly to desktops, tablets, and smartphones without requiring app downloads. Players can review their drawings and the AI's responses on the summary screen or share gameplay links and doodles via integrated social options, facilitating easy dissemination of experiences. These elements contribute to high engagement, as the AI's frequent humorous misinterpretations—such as confusing basic shapes for unrelated objects—often elicit laughter and motivate users to iterate on their sketches, blending entertainment with subtle contributions to machine learning improvement.^[1]^[7]^[17]

Technology

AI Model

The AI model powering Quick, Draw! employs a recurrent neural network (RNN) architecture augmented with convolutional layers to handle the sequential nature of user-drawn strokes in real time. This hybrid design processes input as time-ordered sequences of pen movements (Δx, Δy coordinates and pen states), where convolutional layers extract spatial features from stroke patterns and the RNN captures temporal dependencies across the drawing process. The model is inspired by Google's Sketch-RNN research, which introduced vector-based representations for stroke prediction and generation using RNNs trained on similar doodle data.^[18] The recognition system was developed by Google's Handwriting team, adapting technology from handwritten word recognition to handle sequential doodle inputs.^[3] Training begins with pre-training on a large corpus of historical drawings aggregated from gameplay sessions, enabling the model to learn representations of diverse sketching styles within a fixed vocabulary of approximately 345 categories, such as animals (e.g., cat, dog), objects (e.g., tree, house), and landmarks (e.g., Eiffel Tower). During individual games, the system simulates progressive refinement by incrementally updating predictions as strokes are added, creating the illusion of online learning and adaptation to the user's ongoing input—though the core model remains static per session. This approach draws from Sketch-RNN's autoregressive decoding principles, adapted for classification rather than pure generation.^[4]^[18]^[6] The guessing mechanism operates by outputting a ranked list of probabilities over the 345 categories after processing partial or complete drawings, selecting the top predictions to display dynamically on screen. Over the game's evolution since 2016, model accuracy has increased through periodic retraining on the expanding dataset, now exceeding 50 million drawings, which enhances generalization to varied user inputs. The entire inference process completes in under 20 seconds per game, aligning with the timed drawing challenge and ensuring responsive gameplay.^[7]^[6]

Drawing Recognition

The drawing recognition in Quick, Draw! begins with input preprocessing, where user strokes from mouse or touch interactions are captured as vector sequences consisting of x and y coordinates paired with timestamps in milliseconds since the first point of each stroke. These raw sequences are normalized by aligning the drawing to a top-left origin with minimum values of 0, scaling to a maximum coordinate value of 255, and resampling points at 1-pixel intervals to standardize the data across varying drawing sizes and speeds; this process also simplifies the strokes using the Ramer–Douglas–Peucker algorithm with an epsilon of 2.0 to reduce redundancy while preserving essential shapes.^[4] Although pressure data is not captured in the standard dataset format, the temporal information helps account for drawing speed variations during real-time input.^[14] The preprocessed stroke sequences, converted to differences (Δx, Δy) with indicators for pen states, are then fed directly into the hybrid model. One-dimensional convolutional layers process the sequential data to extract local spatial features from the stroke patterns, transforming the input into a format suitable for the subsequent RNN layers, which model the temporal structure of the drawing.^[4] For real-time analysis, the system processes drawings incrementally as strokes are added, feeding partial sequences into the model to compute evolving probability distributions over possible classes, allowing guesses to update dynamically and often succeeding before the 20-second drawing limit expires.^[3] This incremental processing enables the AI to respond to emerging forms, such as recognizing a circle forming a wheel early in a car sketch. The model demonstrates robustness to variations in user drawings, including imperfect lines, abstract representations, and minor errors, owing to its training on over 50 million diverse human-generated doodles that encompass stylistic differences across global players.^[14] However, it faces limitations with highly abstract or unconventional interpretations that deviate significantly from the training examples, as well as concepts outside the fixed 345-class vocabulary, leading to lower accuracy for ambiguous or rare prompts.^[4]

Dataset

Collection and Size

The Quick, Draw! dataset is assembled from user-generated drawings contributed during gameplay sessions of the online game. Players contribute anonymized drawings by participating, with all data processed in aggregate form for machine learning research and no retention of identifiable information.^[3] These submissions include vector-based stroke data and associated metadata, such as the assigned drawing prompt and the AI's recognition accuracy, all anonymized to exclude any personal identifiers.^[3] Launched in November 2016, the dataset's growth accelerated rapidly through global player engagement. By mid-2017, it encompassed over 50 million drawings across 345 categories, drawn from more than 15 million participants; total collections from gameplay exceeded one billion doodles by December 2017.^[4]^[3]^[5] The publicly released dataset has remained at 50 million drawings since its initial release, with the associated GitHub repository archived on March 11, 2025, indicating no further public updates despite ongoing gameplay.^[4] Drawings are represented in a lightweight vector format as sequences of strokes, each defined by relative coordinates (Δx, Δy), timestamps (Δt), and stroke states (e.g., drawing or endpoint), avoiding raster images to preserve sequential and temporal structure for analysis.^[4] These are organized into 345 predefined classes, such as common objects like "cat" or "airplane," enabling straightforward categorization.^[4] Ethical practices emphasize user privacy and consent, with all data processed in aggregate form for machine learning research and no retention of identifiable information.^[3] The dataset is hosted on Google Cloud Platform, utilizing services like Cloud Storage for raw files and BigQuery for querying, with quality maintenance involving the removal of incomplete or invalid strokes during processing.^[3]

Public Access

The Quick, Draw! dataset has been freely available for download since its release in 2017, enabling researchers, developers, and artists to access the collection without cost through official channels. It is hosted on Google Cloud Storage at gs://quickdraw_dataset/ and can be retrieved using tools like gsutil, with the full archive maintained for ongoing access. The dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), which permits broad reuse as long as attribution is given to Google Creative Lab.^[4]^[19] Data is provided in multiple formats to suit different processing needs, including NDJSON files for raw stroke data, simplified NDJSON for reduced complexity, binary (.bin) files, and NumPy (.npy) arrays representing 28x28 grayscale bitmaps for machine learning workflows. Specialized subsets, such as the Sketch-RNN format in .npz files containing 75,000 samples per category, and category-specific collections like drawings of "The Mona Lisa," allow targeted exploration without handling the entire set. The complete dataset, encompassing over 50 million drawings across 345 categories, is available for download.^[4] Google provides supporting tools to facilitate usage, including TensorFlow tutorials for recurrent neural network training on the data, Python and Node.js parsers for handling NDJSON files, and a Data API that enables querying individual drawings by class label or similarity without requiring a full download. Interactive demos on the Quick, Draw! website allow users to visualize and replay drawings, while integrations with frameworks like TensorFlow support seamless incorporation into machine learning pipelines. Usage is encouraged for non-commercial research and educational purposes, with Google recommending contact via [email protected] for project notifications or collaborations.^[4]^[20]^[21] Periodic snapshots of the dataset have been released since 2017, with the most recent full archive dated March 11, 2025, ensuring stability for long-term studies while preserving historical versions on Google Cloud Storage.^[4]

Applications and Legacy

Machine Learning Uses

The Quick, Draw! dataset has primarily been utilized to train sketch-based generative models, such as Sketch-RNN, which employs a recurrent neural network to learn and generate vector drawings from partial user inputs, enabling applications like doodle completion and style transfer between different sketch categories.^[22] In Sketch-RNN, the model autoregressively predicts stroke sequences conditioned on class labels or prior strokes, allowing it to extend incomplete sketches in real-time while preserving human-like variability in drawing styles.^[22] This approach has facilitated interactive tools where users collaborate with AI to refine or transform doodles, demonstrating the dataset's value for sequential data modeling in generative AI.^[23] Beyond core generative tasks, the dataset has supported research in vector graphics generation, where models like BézierSketch leverage its stroke-based representations to produce scalable, parametric sketches using Bézier curves for smoother, editable outputs suitable for graphic design.^[24] For gesture recognition, adaptations of the dataset have informed models that interpret dynamic hand-drawn inputs as sequences, aiding in real-time classification of symbolic gestures for interactive systems. In few-shot learning, the Quick, Draw! data serves as a benchmark within meta-learning frameworks like Meta-Dataset, where it tests algorithms' ability to classify novel sketch categories from limited examples, highlighting challenges in adapting to sparse, noisy visual data. Google publications, such as Sketch-RNN, exemplify these uses by integrating the dataset into multimodal AI experiments for enhanced sketch understanding.^[22] In industry contexts, the dataset has influenced educational AI demonstrations for stroke prediction, powering tools that anticipate and suggest drawing completions to teach users about neural network inference in creative workflows. Extensions include adaptations for accessibility, and artistic AI systems that generate doodle-based art by sampling from learned distributions. Overall, the Quick, Draw! dataset has enabled key benchmarks for real-time sketch AI, establishing standards for evaluating latency and accuracy in sequential recognition tasks across over 345 categories.^[25] By 2025, it has garnered over 1,000 citations in academic literature, underscoring its high-impact role in advancing sketch-based machine learning.^[26]

Reception and Impact

Upon its launch in November 2016, Quick, Draw! received widespread praise from media outlets for democratizing artificial intelligence through an engaging, accessible format. Publications such as Wired highlighted the game's impressive neural network capabilities, describing it as a modern take on Pictionary that effectively showcased AI's potential in real-time recognition tasks.^[27] Similarly, the Huffington Post emphasized its intuitive design, noting how it allowed users to interact directly with machine learning in a playful manner.^[28] Bustle further underscored its educational merits, portraying it as a tool that made complex concepts like neural networks approachable and entertaining for non-experts.^[29] The game quickly garnered significant user engagement, with over 15 million players contributing millions of drawings by early 2017, a figure that grew to exceed one billion doodles across 345 categories by late 2017.^[5]^[6] Its popularity was sustained through viral sharing on social platforms and word-of-mouth, though it did not receive major industry awards. While not prominently featured in mainstage Google I/O keynotes, the game's success contributed to broader discussions on interactive AI during Google events. Culturally, Quick, Draw! helped popularize the concept of "doodle AI" in mainstream awareness, inspiring subsequent tools like Google's AutoDraw, which leveraged the amassed dataset to assist users in refining sketches into polished icons.^[30] It has also found a place in educational settings, where educators use it to introduce machine learning principles, such as neural network training, through hands-on activities that encourage students to explore AI's interpretive limitations.^[16] Critics noted some limitations, including the game's fixed vocabulary of 345 categories, which constrained its scope compared to more open-ended drawing applications.^[4] Early reviews raised concerns about data privacy, particularly regarding the collection of user drawings, but these were addressed through explicit opt-in mechanisms that required player consent before any data was stored or used for training.^[3] As of 2025, Quick, Draw! endures as a benchmark for interactive AI demonstrations, continuing to serve as an entry point for public engagement with machine learning while influencing conversations on ethical data practices, such as consent-based collection in crowdsourced datasets.^[7]

References

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Quick, Draw! by Google Creative Lab
This is a game built with machine learning. You draw, and a neural network tries to guess what you're drawing. Of course, it doesn't always work.
[2]
Google's AI Proves That Your Drawings Look Like Everyone Else's
May 24, 2017 · In November 2016, Google released a cute little game called Quick, Draw! on its AI Experiments website, where it showcases fun or unusual AI ...
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Drawings in the Cloud: introducing the Quick, Draw! dataset
Dec 8, 2017 · Quick, Draw! is an AI experiment that has delighted millions of people across the world. The game asks users to draw a doodle, then the game's AI tries to ...
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Documentation on how to access and use the Quick, Draw! Dataset.
Mar 1, 2017 · The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!
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Quick, Draw! The Data
What do 50 million drawings look like? Over 15 million players have contributed millions of drawings playing Quick, Draw! These doodles are a unique data ...The Mona Lisa · Quickdraw logo · Aircraft carrier · 103031 cat drawingsMissing: size 2025
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People have drawn one billion doodles in Quick, Draw! - The Keyword
Dec 8, 2017 · Quick, Draw! began as a simple way to let anyone play with machine learning. But these billions of drawings are also a valuable resource for ...<|control11|><|separator|>
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Quick, Draw! - Google
Help teach it by adding your drawings to the world's largest doodling data set, shared publicly to help with machine learning research.
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This Genius A.I. Game Can Guess Whatever You Draw - Thrillist
Nov 17, 2016 · Quick, Draw! is one of Google's newly released AI Experiments, developed for the purpose of demonstrating machine learning and neural networks through ...
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Quick, Draw! - Kotaku
Quick, Draw! Game Details. Available on: Android; browser. Genres. Quiz/Trivia. Developer. Google. Release Date. November 14, 2016 (8 years ago). Publisher.
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The Dead-Serious Strategy Behind Google's Silly AI Experiments
Dec 1, 2017 · And while the Creative Lab team says their only new intended use for the Quick, Draw! data set is to make a T-shirt out of T-shirt drawings, ...
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Quick Draw! - Jonas Jongejan
Nov 14, 2016 · Quick, Draw! is a game released as one of the AI Experiments launched by Google Creative Lab. I came up with the idea for the game during an internal hackathon.Missing: history | Show results with:history
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Exploring Quick, Draw! - an online game by Google - IndiaAI
Oct 6, 2022 · Google released an online game called Quick, Draw! in November 2016. The game aims to draw a given object in less than 20 seconds. But this ...
[13]
How Does Google "Quick, Draw!" Work? - Bustle
Nov 23, 2016 · To play the game, head over to the "Quick, Draw!" site, where you'll be given six different prompts, one after another. The prompts are ...
[14]
Introducing the Kaggle “Quick, Draw!” Doodle Recognition Challenge
Sep 28, 2018 · We have launched the Kaggle “Quick, Draw!” Doodle Recognition Challenge, which tasks participants to build a better machine learning classifier for the ...
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This Google-powered AI can identify your terrible doodles - The Verge
Nov 15, 2016 · Quick, Draw! is a great way to familiarize yourself with how neural networks work to identify objects and text in photos.Missing: obscure | Show results with:obscure
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Quick, Draw! with Google – TCEA TechNotes Blog
Mar 5, 2017 · Quick, Draw!, which launched last November, is a game built with machine learning. Players are prompted to sketch an object on a 20-second clock ...
[17]
Google Quick, Draw! is a fun new game for the A.I. Experiment
Nov 3, 2016 · Quick, Draw!, which is just one of the features in Google's new AI Experiments initiative, gives players a favorite new mindless activity.
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[PDF] A Neural Representation of Sketch Drawings - arXiv
We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on a dataset of.
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[PDF] Quick, Draw! Doodle Recognition - CS229
In November 2016, Google released an online game ti- tled Quick, Draw ... Google publicly released a Quick, Draw! dataset con- taining over 50 million ...Missing: launched | Show results with:launched
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Assignment 6. I played around with Quick, Draw! and I… - Medium
Oct 14, 2019 · By clicking on “Let's Draw!” button below the consent message, the individuals are giving consent to the collection and use of their data. If ...
[21]
Any plan to update the dataset ? · Issue #67 - GitHub
Apr 22, 2021 · The GCP folder shows that the raw data have been modified last in 2017. Any plan to update them with the data collected after that time ?
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https://arxiv.org/abs/1704.03477
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quickdraw_bitmap | TensorFlow Datasets
Jun 1, 2024 · The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!Missing: total GB
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First, in 2016, there was Quick, Draw!, which uses a neural network to guess what you're drawing. Since Quick, Draw! launched we have collected ...
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[1704.03477] A Neural Representation of Sketch Drawings - arXiv
Apr 11, 2017 · Sketch-rnn is a recurrent neural network (RNN) that constructs stroke-based drawings of common objects, trained on human-drawn images.
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Jun 26, 2017 · We made an interactive web experiment that lets you draw together with a recurrent neural network model called sketch-rnn.Missing: AI architecture
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[PDF] BézierSketch: A generative model for scalable vector sketches
Dataset. Quick, Draw! is a large sketch dataset [8] collected as a part of an online game to draw a given category within a time-limit, in which thousands of ...<|separator|>
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Drawing with Students Who are Visually Impaired (QuickDraw)
May 31, 2024 · It is a game by Google where you are given a word and given 20 seconds to try to Doodle it, while the Google AI tries to guess what you're drawing.
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### Summary of Quick, Draw! Dataset
[31]
Whoa, Google's AI Is Really Good at Pictionary - WIRED
Nov 18, 2016 · Take the game, Quick, Draw! It works like Pictionary; the game gives you 20 seconds to draw an object on screen, and Google shouts out guesses ...
[32]
Let A Computer Guess What You're Drawing In This High-Tech ...
Nov 21, 2016 · An online game where a computer guesses what you're doodling based on prompts including “motorbike,” “houseplant,” “pizza,” “foot” and more.Missing: reception Wired Bustle
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Nov 21, 2016 · Each assigned object varies in terms of difficulty and detail, though they are all definitely sketchable. The algorithm will guess at the object ...
[34]
Google Is Using Artificial Intelligence for Clip Art - The Atlantic
Apr 12, 2017 · Each round of the game involves six sketching prompts, with users getting 20 seconds to draw the assigned subject matter—ant! calculator ...<|control11|><|separator|>