Slashdot Mirror
Breakthrough In Automatic Handwritten Character Recognition Sans Deep Learning (technologyreview.com)
subh_arya writes: Researchers from NYU, UToronto and MIT have come up with a technique that captures human learning abilities for a large class of simple visual concepts to recognize handwritten characters from World's Alphabet. Their computational model (abstract) represents concepts as simple programs that best explain observed examples under a Bayesian criterion. Unlike recent deep learning approaches that require thousands of examples to train an efficient model, their model can achieve human-level performance with only one example. Additionally, the authors present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.
Maybe they'll also invent a better way to untangle corded phone cables.
I hope this heralds in some significant improvements to basic OCR. It amazes me that OCR against a printed document still doesn't always yield 100% success. Even worse are OCRs on printed music manuscripts. The recognition and transcription quality is atrocious.
And yet, these guys can recognise handwriting with incredible accuracy.
I keenly await when these algorithms can be expanded to general OCR / document recognition. Even if there need to be specific models for each type of document.
It's cretin, you cretin.