Steps for recognizing hand written signs What are the steps to built a system which recognizes hand written signs in an image and returns the most similar sign in Python? I know that depends on the image and many variables, but isn't there a sequence of steps, a main path to follow for recognizing images with at most 2 or 3 signs per image? 

I thought the steps should be:


Take an image and convert to binary
Apply find_contour to binary image
Extract scale- and rotation-invariant properties
Create a feature vector with scale/rotation-invariant properties (if there is much more than 1 sign, like for example a star and a square not overlapped, is that a problem?)
Repeat steps 1 to 4 for every image
Put them into a k-NN or SVM


These next steps, I do not know how to implement:


I choose an image
System spits out the result (most similar image)
That's what you would do with images, not so much with handwriting, which needs more preprocessing and robust features because people don't write exactly the same way twice. See for example:


Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models
Adaptive Membership Functions for Handwritten Character Recognition by Voronoi-Based Image Zoning
A visual approach to sketched symbol recognition
Feature extraction and classifier combination for image-based sketch recognition
Sketched symbol recognition with auto-completion
HBF49 feature set: A first unified baseline for online symbol recognition
Neural network-based symbol recognition using a few labeled samples