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    <p>Given the possible variation in handwritten inputs I would suggest that a neural network approach is the way to go; you will find it difficult or impossible to accurately model these classes by hand. LastCoder's attempt works to a degree, but it does not cope with much variation or have promise for high accuracy if worked on further - this kind of hand-engineered approach was abandoned a very long time ago.</p> <p>State-of-the-art results in handwritten character classification these days is typically achieved with <a href="http://ufldl.stanford.edu/tutorial/index.php/Convolutional_Neural_Network" rel="nofollow">convolutional neural networks (CNNs)</a>. Given that you have only 3 classes the problem <em>should</em> be easier than digit or character classification, although from experience with the <a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow">MNIST</a> handwritten digit dataset, I expect that your circles, squares and diamonds may occasionally end up being difficult for even humans to distinguish.</p> <p>So, if it were up to me I would use a CNN. I would input binary images taken from the drawing area to the first layer of the network. These may require some preprocessing. If the drawn shapes cover a very small area of the input space you may benefit from bulking them up (i.e. increasing line thickness) so as to make the shapes more invariant to small differences. It may also be beneficial to centre the shape in the image, although the <a href="http://ufldl.stanford.edu/tutorial/index.php/Pooling" rel="nofollow">pooling</a> step might alleviate the need for this.</p> <p>I would also point out that the more training data the better. One is often faced with a trade-off between increasing the size of one's dataset and improving one's model. Synthesising more examples (e.g. by skewing, rotating, shifting, stretching, etc) or spending a few hours drawing shapes may provide more of a benefit than you could get in the same time attempting to improve your model.</p> <p>Good luck with your app!</p>
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    1. COThanks, I have already quite a lot of training data (samples from about 10 people, every one making about 5-10 of every shape). I tried conventional NN as was taught on mine course and failed. I will definitly check this one. Thanks for advice with bulking lines up.
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    2. COThat dataset seems quite small. You may find your networks overfitting without more data.
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    3. COHm... I thought it may be enough for research purpose, but if you say so, I can get more. How many samples would you suggest as a good size?
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