<p>There's an <strong>MNIST sequence dataset</strong> from Edwin de Jong:</p>

<ul>
<li>Paper: <a href="https://arxiv.org/pdf/1611.03068.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1611.03068.pdf</a></li>
<li>Github: <a href="https://edwin-de-jong.github.io/blog/mnist-sequence-data/" rel="nofollow noreferrer">https://edwin-de-jong.github.io/blog/mnist-sequence-data/</a></li>
<li>Blog: <a href="https://github.com/edwin-de-jong/mnist-digits-as-stroke-sequences/" rel="nofollow noreferrer">https://github.com/edwin-de-jong/mnist-digits-as-stroke-sequences/</a></li>
</ul>

<p>and an <strong>MNIST classification using RNN</strong> by Ryan Epp:</p>

<ul>
<li><a href="https://www.ryanepp.com/blog/mnist-classification-using-stroke-paths" rel="nofollow noreferrer">https://www.ryanepp.com/blog/mnist-classification-using-stroke-paths</a></li>
</ul>

<p>In both projects, the direction strokes will take at a T-junction depends on the algorithm and is often counterintuitive. This means that there's more to learn for sequences since many stroke patterns will produce the same image.</p>

<p><a href="https://i.stack.imgur.com/hu3Jp.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/hu3Jp.png" alt="digit 4 as a sequence"></a></p>
MNIST dataset conversion
<p><img src="https://i.stack.imgur.com/KnBXM.jpg" alt="Trained image"> I've trained a decision tree on a dataset (handwritten) which contains 8 x-y points sampled along the length of the number (number digit dataset). The test dataset given (assignment), is the MNIST dataset, which is the pixel intensities in a 28x28 bitmap image. I need to sample 8 points and along the trajectory of the number so that it performs well.</p>

<p>I'm doing this in Python. I don't know what to do with the image to sample those points. Any package/procedure will help.</p>