Reading digits in natural images with unsupervised feature learning

Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features.

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• 1 Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits.
• 2 Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks.
• 3 In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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