<p>It looks like that it used convolution and recurrent neural network. There is <a href="https://github.com/googlecreativelab/quickdraw-dataset" rel="nofollow noreferrer">a dataset webpage used in Quick Draw project</a> and it led to <a href="https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/recurrent_quickdraw.md" rel="nofollow noreferrer">Recurrent Neural Networks for Drawing Classification GitHub page</a>. The GitHub link showed the neural network structure as well.</p>
<p>Here is <a href="https://camo.githubusercontent.com/195ad70dcef32fe44a143a8b65609d19622271052256cd7c6e904c44caf4ac6c/68747470733a2f2f7777772e74656e736f72666c6f772e6f72672f696d616765732f717569636b647261775f6d6f64656c2e706e67" rel="nofollow noreferrer">the neural network structure from the GitHub page</a>:<br />
<a href="https://i.sstatic.net/1KVRW.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/1KVRW.png" alt="enter image description here" /></a></p>
<p>The current Quick Draw might be evolved from the neural network structure at the GitHub because the webpage written in 2020.</p>

CC BY-SA 4.0
2022-02-10T19:11:33.590Z
2022-02-10T19:18:18.707Z
2022-02-10T19:18:18.707Z
Cloud Cho
<p>I believe they don't use CNNs. The most important reason why it's because they have more information than a regular image: time. The input they receive is a sequence of (x,y,t) as you draw on the screen, which they refer as "ink". This gives them the construction of the image for free, which a CNN would have to deduce by itself.</p>

<p>They tried two approaches. Their currently most successful approach does the following:</p>

<ul>
<li>Detect parts of the ink that are candidates of being a character</li>
<li>Use a FeedForward Neural Network to do character recognition on those candidates</li>
<li>Use beam search and a language model to find most the most likely combination of results that results into a word</li>
</ul>

<p>Their second approach is using an LSTM (a type of Recurrent Neural Network) end-to-end. In their paper they say this was better in a couple languages.</p>

<p><strong>Source</strong>: I was an intern in Google's handwriting team in summer 2015 (on which I believe quickdraw is based), but the techniques I explained can be found in <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=7478642" rel="nofollow noreferrer">this paper</a>.</p>

CC BY-SA 3.0
2016-11-19T22:19:06.493Z
2017-01-07T15:47:06.297Z
2017-01-07T15:47:06.297Z
etal
<p>Does anyone know, or can we deduce or infer with high probability from its characteristics, whether the neural network used on this site </p>

<p><a href="https://quickdraw.withgoogle.com/" rel="nofollow noreferrer">https://quickdraw.withgoogle.com/</a></p>

<p>is a type of convolutional neural network (CNN)?</p>

CC BY-SA 3.0
2018-01-01T07:51:38.103Z
Checkout https://github.com/tensorflow/magenta/tree/master/magenta/models/sketch_rnn
Ronak Poriya
CC BY-SA 4.0
2022-02-10T18:33:15.977Z
@RonakPoriya Is the link based on Google Quick Draw code or part of the project?
Cloud Cho
<neural-networks><image-recognition><convolutional-neural-networks>
Is the QuickDraw with Google neural net a convolutional neural network?
dynrepsys
# Is the QuickDraw with Google neural net a convolutional neural network?

(Asked by: username_0 on 2016-11-18)

Does anyone know, or can we deduce or infer with high probability from its characteristics, whether the neural network used on this site

<https://quickdraw.withgoogle.com/>

is a type of convolutional neural network (CNN)?

**Tags:** neural-networks, image-recognition, convolutional-neural-networks

> **Comments:**
> **username_1**: Checkout https://github.com/tensorflow/magenta/tree/master/magenta/models/sketch_rnn  
> **username_2**: @RonakPoriya Is the link based on Google Quick Draw code or part of the project?  

---

## 2 answers

### Answer by username_3 on 2016-11-19. Score: 6 | Accepted answer

I believe they don't use CNNs. The most important reason why it's because they have more information than a regular image: time. The input they receive is a sequence of (x,y,t) as you draw on the screen, which they refer as "ink". This gives them the construction of the image for free, which a CNN would have to deduce by itself.

They tried two approaches. Their currently most successful approach does the following:

* Detect parts of the ink that are candidates of being a character
* Use a FeedForward Neural Network to do character recognition on those candidates
* Use beam search and a language model to find most the most likely combination of results that results into a word

Their second approach is using an LSTM (a type of Recurrent Neural Network) end-to-end. In their paper they say this was better in a couple languages.

**Source**: I was an intern in Google's handwriting team in summer 2015 (on which I believe quickdraw is based), but the techniques I explained can be found in [this paper](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7478642).


---

### Answer by username_2 on 2022-02-10. Score: 0

It looks like that it used convolution and recurrent neural network. There is [a dataset webpage used in Quick Draw project](https://github.com/googlecreativelab/quickdraw-dataset) and it led to [Recurrent Neural Networks for Drawing Classification GitHub page](https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/recurrent_quickdraw.md). The GitHub link showed the neural network structure as well.

Here is [the neural network structure from the GitHub page](https://camo.githubusercontent.com/195ad70dcef32fe44a143a8b65609d19622271052256cd7c6e904c44caf4ac6c/68747470733a2f2f7777772e74656e736f72666c6f772e6f72672f696d616765732f717569636b647261775f6d6f64656c2e706e67):  
[![enter image description here](https://i.sstatic.net/1KVRW.png)](https://i.sstatic.net/1KVRW.png)

The current Quick Draw might be evolved from the neural network structure at the GitHub because the webpage written in 2020.


---

