Google team’s neural network approach works on street numbers

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Posted January 10, 2014
Google team’s neural network approach works on street numbers
Difficult but correctly transcribed examples from the internal street numbers dataset. Some of the challenges in this dataset include diagonal or vertical layouts, incorrectly applied blurring from license plate detection pipelines, shadows and other occlusions. Credit: arXiv:1312.6082 [cs.CV]
A Google team has worked out a neural network approach to transcribe house numbers from Street View images, reading those house numbers and matching them to their geolocation. Google Street View has the user advantage of allowing the user to advance to street level to see the area of interest in detail. Google’s accomplishment in automation is impressive both in the scope of the task involved and the way in which it was done. Consider that Google’s Street View cameras have recorded massive numbers of panoramic images carrying massive numbers of house numbers. “We can for example transcribe all the views we have of street numbers in France in less than an hour using our Google infrastructure,” said the researchers, who have authored the paper, “Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks.” Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay Shet are the authors.

The paper was submitted to arXiv and was explored in a report earlier this week in MIT Technology Review, which examines their research.


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