Question about handwritten numbers recognition with limited data set I have a an assignment to make a classifier for hand-written numbers with a limited data set of 500 samples.
I am currently using python, I tried sklearn SVC with linear classifier and I got an accuracy of about 80%. I also tried KNN with 13 neighbors and got a max of 81% accuracy.
I need to get to at least 90% accuracy. What are your suggestions that would help with training (adding noise? what distribution? / tilting the pictures with 90 degrees? Maybe cross validating over different parameters... etc).

Edit:
To clarify the main challenge:
I have to reach an accuracy of more that 90% with only using 500 samples, I can play around with the samples however I want to extend the data but I am not allowed to use external samples.
I suggest you the following:


Adding a little bit of noise such as blurring
Transforming the images. You can rotate or stretch a bit, but make sure you don't change too much to change 6 to 9 or o to 0.


You can look at the following tutorials: 


https://stackoverflow.com/questions/14177744/how-does-perspective-transformation-work-in-pil
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.html
http://effbot.org/imagingbook/image.htm