Detect symbols written on a whiteboard using OpenCV

I'm trying to detect shapes written on a whiteboard with a black/blue/red/green marker. The shapes can be circles, rectangles or triangles. The image can be found at the bottom of this post.

I'm using OpenCV as the framework for the image recognition.

My first task is to research and list the different strategies that could be used for the detection. So far I have found the following:

1) Grayscale, Blur, Canny Edge, Contour detection, and then some logic to determine if the contours detected are shapes?

2) Haar training with different features for shapes

3) SVM classification

4) Grayscale, Blur, Canny Edge, Hough transformation and some sort of color segmentation?

Are there any other strategies that I have missed? Any newer articles or tested approaches? How would you do it?

One of the test pictures: https://drive.google.com/file/d/0B6Fm7aj1SzBlZWJFZm04czlmWWc/view?usp=sharing

UPDATE: The first strategy seems to work the best, but is far from perfect. Issues arise when boxes are not closed, or when the whiteboard has a lot of noise. Haar training does not seems very effective because of the simple shapes to detect without many specific features. I have not tried CNN yet, but it seems most appropriate to image classification, and not so much to detect shapes in a larger image (but I'm not sure)

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I think that the first option should work. You can use fourier descriptors in order to classify the segmented shapes.

http://www.isy.liu.se/cvl/edu/TSBB08/lectures/DBgrkX1.pdf

Also, maybe you can find something useful here:

http://www.pyimagesearch.com/2016/02/08/opencv-shape-detection/

If you want to try a more challenging but modern approach, consider deep learning approach (I would start with CNN). There are many implementations available on the internet. Although it is probably an overkill for this specific project, it might help you in the future...

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