This hand-tracking algorithm could lead to sign language recognition

This hand-tracking algorithm could lead to sign language recognition

Millions of people communicate using sign language, but so far projects to capture its complex gestures and translate them to verbal speech have had limited success. A new advance in real-time hand tracking from Google’s AI labs, however, could be the breakthrough some have been waiting for.

The new technique uses a few clever shortcuts and, of course, the increasing general efficiency of machine learning systems to produce, in real time, a highly accurate map of the hand and all its fingers, using nothing but a smartphone and its camera.

“Whereas current state-of-the-art approaches rely primarily on powerful desktop environments for inference, our method achieves real-time performance on a mobile phone, and even scales to multiple hands,” write Google researchers Valentin Bazarevsky and Fan Zhang in a blog post. “Robust real-time hand perception is a decidedly challenging computer vision task, as hands often occlude themselves or each other (e.g. finger/palm occlusions and hand shakes) and lack high contrast patterns.”

Not only that, but hand movements are often quick, subtle or both — not necessarily the kind of thing that computers are good at catching in real time. Basically it’s just super hard to do right, and doing it right is hard to do fast. Even with multi-camera, depth-sensing rigs like those used by SignAll have trouble tracking every movement. (But that isn’t stopping them.)

The researchers’ aim in this case, at least partly, was to cut down on the amount of data that the algorithms needed to sift through. Less data means quicker turnaround.

handgesturesFor one thing, they abandoned the idea of having a system detect the position and size of the whole hand. Instead, they only have the system find the palm, which is not only the most distinctive and reliably shaped part of the hand, but is square, to boot, meaning they

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