Might be a tad simplistic but Googling "bibtex + paper title" ussualy gets you a formated bibtex entry from the ACM,Citeseer, or other such reference tracking sites. Ofcourse this is assuming the paper isn't from a non-computing journal :D
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I have a feeling you won't find a custom solution for this, you might want to write to citation trackers such as citeseer, ACM and google scholar to get ideas for what they have done. There are tons of others and you might find their implementations are not closed source but not in a published form. There is tons of research material on the subject.
The research team I am part of has looked at such problems and we have come to the conclusion that hand written extraction algorithms or machine learning are the way to do it. Hand written algorithms are probably your best bet.
This is quite a hard problem due to the amount of variation possible. I suggest normalizing the PDF's to text (which you get from any of the dozens of programmatic PDF libraries). You then need to implement custom text scrapping algorithms.
I would start backward from the end of the PDF and look what sort of citation keys exist -- e.g., [1], [author-year], (author-year) and then try to parse the sentence following. You will probably have to write code to normalize the text you get from a library (removing extra whitespace and such). I would only look for citation keys as the first word of a line, and only for 10 pages per document -- the first word must have key delimiters -- e.g., '[' or '('. If no keys can be found in 10 pages then ignore the PDF and flag it for human intervention.
You might want a library that you can further programmatically consult for formatting meta-data within citations --e.g., itallics have a special meaning.
I think you might end up spending quite some time to get a working solution, and then a continual process of tuning and adding to the scrapping algorithms/engine.
