The problem is a bit different than traditional handwriting recognition. I have a dataset that are thousands of the following. For one drawn character, I have several sequenti
This problem is actually a mix of two problems:
A HMM is used for finding the most likely sequence of a finite number of discrete states out of noisy measurements. This is exactly problem 2, since noisy measurements of discrete states a-z,0-9 follow eachother in a sequence.
For problem 1, a HMM is useless because you aren't interested in the underlying sequence. What you want is to augment your handwritten digit with information on how you wrote it.
Personally, I would start by implementing regular state-of-the-art handwriting recognition which already is very good (with convolutional neural networks or deep learning). After that, you can add information about how it was written, for example clockwise/counterclockwise.