It is a surmountable problem for someone experienced in software architecture and machine learning.
- Render the message to a virtual display such as xvfb, headless Chrome, or phantomjs.
- Capture the text with selenium, watir, or some other DOM controller, addressing your HTML and DHTML complexity concern.
- OCR the text in inline images and insert it appropriately.
- Once you have text with only word, line, list item, and paragraph breaks as structural separators, you have adequate separation of style and language content to then use naive Bayesian or one of the more recent forms of unsupervised categorization to find the separation point between the body and the signature block.
Extending your line of thinking, you may even be able to engineer a generative strategy for automated reply, but beware, this last feat is a dozen orders of magnitude more difficult than extracting text from HTML, DHTML, and typeset images and machine learning the separating signature blocks.
This last feat, if done poorly, would get you in trouble with many of your email reply recipients, and, if done well, would place you ahead of Amazon, Apple, and Google.