Using neural networks for rotation invariant OCR
Aha, this is a quite funny story that I've heard about CV. Are you a bio guy? Any way, here are my suggestions. 


If you are a bio guy and just want to finish this project ( I mean successfully identify each insect in a video frame ), go for barcode, QR etc. They are labels though their contents are not directly readable by eyes. However, you will have a much better chance to handle all kind of problems you might encountered when you use manual labels, e.g. occlusion, error correction, lighting etc. There are libraries can decode these labels, and you simply call them. 


In this way, you will save a lot of energy to not working on something you are in lack of experience, but use the state-of-the-art technologies. 


If you are not a bio guy or these manual labels are not something can be changed, you should first locate these labels and then recognize them. Donot mess up these two things. The first task is detection, and the second one is recognition. In other words, you need to do two things:
a). Train a text/none-text classifier. In this classifier you can use those rotational invariant features. 
b). Train a character classifier ( e.g. using NN ) to decide the class of a character candidate you found from (a).


By the way, I donot believe you need to train a NN accepting character at an arbitrary angle. Instead, all you need is to train a NN accepting normal characters, and normalize a candidate using image moments or other techniques before you feed it into the classifier. Or simply rotate your candidate to a certain degree (e.g. for every 10 degree) and then feed them into your classifier and pick the result with the highest confidence.