Been there, done that. One of our on-going projects is to create a process for processing nutrition labels from iPhone camera for a health-tracking app. My company decided to develop a solution for this particular application - extracting data from US nutrition labels. This solution will be used for this client, but we decided to pre-package a few other flexible capabilities along the way, to be used by a wider audience. The solution will be available to general public in about 3 weeks. (I'll come back here and post an update.)
Main goal that will make or break the entire idea is to get usable images. Curved images, heavy shading, low resolution, and blur will all substantially decrease OCR quality, often to no quality. In apps, we found that user training and guidance is the most helpful method. Then there are some technical tools such as quickly detecting quality of image and suggesting to re-take it if needed. In general, if you can achieve "high quality fuel" (images) you can expect high performance from your "machine".
Next, the OCR. Tesseract can do good job reading clear and simple test such as pages from books. Commercial products such as ABBYY distinguish themselves by working well on tough images - shading, distortions, small prints, etc. And unfortunately if mobile images are used, they are more often on bad side than good quality side.
Next, pick your approach to locating and extracting data. Please see my answer on text parsing vs specialized test extraction tools:
For our project we'll be using ABBYY FlexiCapture for targeted nutrition data. It has special tools even when OCR makes mistakes to still find appropriate data (sort of controlled fuzzy search), and that was an important factor in selecting it for the task.