Shape Detection using Machine Learning

Question

I would like to detect shapes namely circle, square, rectangle, triangle, etc., using Machine Learning Techniques.

Following are the specifications for shape detection,

1. Convolutional Neural Network ( CNN ) is used.
2. For Training, Dataset contains 1000 images in each category for 10 shapes.
3. For Testing, Dataset contains 100 images in each category for 10 shapes.
4. All images are 28x28 resize with one channel ( gray channel ).
5. All the images in the dataset are edge-detected images.

Questions

1. Is it possible for the machine learning algorithm to differentiate between a square and a rectangle...?, square and a rhombus...?
2. How can i improve the dataset for shape detection ? Thanks in Advance...!!!

Answer 1

1. Yes, and it is not a very hard task for a CNN to do.
2. One way to improve the dataset is to use image augmentation. I think you can do both horizontal and vertical flips as all these figures are still the same kind of figures when applying this transformation. You can think of other transformations as long as they don't change the axes sizes, because if you change the sizes of the axes a square becomes a rectangle, and viceversa.