In addition to Emre's suggestions, I'll add my favorite textbook on the subject:
Character Recognition Systems by Cheriet, et al.
An earlier but still useful work is that of Bunke, but used copies can be very expensive (check www.addall.com) so you might want to find one in an engineering library.
Handwriting recognition is an extraordinarily difficult problem in the general case. It's necessary to distinguish between analysis of handwriting on the fly ("online") and analysis/recognition of handwriting on paper or in a scanned image ("offline").
Try to write the most detailed specification you can about what it is you want to achieve, including the following:
- script & language (e.g. English, Hindi, traditional Chinese, Hangul,...)
- image acquisition method (e.g. scanned paper, smart phone picture,...)
- size of dictionary of symbols to be matched (10? 500? 7000?)
- OCR character database used to measure performance
- means to train new symbols - can the user do it?
- target platform (phone? desktop? custom device? multiple devices?)
- amount of time you want to spend on development
This problem has tortured researchers for decades, and although great progress has been made on many fronts it's exceedingly difficult to do well.
For OCR problems in particular I recommend spending a few days trying to design the best algorithm you can based only on what you already know. Implement the algorithm, test it, and struggle with it. Find out what doesn't work. Rewrite your specification, and then dig up a good textbook on OCR and read it cover to cover.
Also, read this book to learn more about how humans can read characters and recognize symbols:
Reading in the Brain by Stanislas Dehaene.
If you can define a very narrow scope for your recognition algorithm, set modest goals for accuracy, read up on standard methods, and then give the problem at least a few months then I'm sure you'll learn quite a bit.