I've used visual recognition as part of a strategy for metadata capture in several environments - it's just one more tool that can be a force multiplier. Sure, at 50% error rate (which is arbitrary, mind you, and can be much better or worse depending on environment), this is not acceptable for unattended metadata capture; but if you were presenting suggested terms to a cataloger based on a visual recognition engine trained against your own data/assets, even if 50% of the terms are garbage that can be a huge productivity boost, not to mention an improvement in the depth of cataloging.
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