

How Google Cracked House Number Identification in Street View - nkurz
http://www.technologyreview.com/view/523326/how-google-cracked-house-number-identification-in-street-view/

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sanxiyn
This is a tangent, but:

"That's particularly useful in places where street numbers are otherwise
unavailable or places such as Japan and South Korea where streets are rarely
numbered in chronological order but in other ways such as the order in which
they were constructed, a system that makes many buildings impossibly hard to
find, even for locals."

South Korea finished renumbering streets in 2011 and after two and a half
years of trial completely switched to the new system in January 2014.

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trollian
The sentence doesn't make sense. Japan and (apparently formerly) South Korea
use chronological rather than geographical ordering.

~~~
sanxiyn
Yeah, obviously "chronological order" in the original sentence is a mistake
for "geographical order".

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giovannibajo1
In Florence, Italy, in the historical city center, we have an unique numbering
system; each street has two series of independent numbering of buildings,
differentiated by _colors_ : red numbers are for businesses, black numbers for
houses. So for instance a restaurant could be located on the number "23r"
(r=red), while the standard "23" (black) can be hundreds of meters away in the
same street.

I think there is currently no mapping system that handles this madness. Google
Maps still does a decent job if you're looking for a specific place, because
people have reported the exact gps positions of most businesses through user-
reporting, but if you enter an address with a red number, you're unlikely to
be correctly directed.

I guess the neural network knows nothing of colors...

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Vik1ng
[http://www.openstreetmap.org/#map=18/43.77296/11.25726](http://www.openstreetmap.org/#map=18/43.77296/11.25726)

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nl
I haven't read the paper yet, but I don't think this is even the biggest CNN
inside Google. The NIPS2015 Hilton/Dean paper talks about a single network
trained for image classification for _six months_ on a _large number of
cores_.

~~~
nerderloo
This article is almost a year old(January 6, 2014)

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dclowd9901
I had thought they just used Captcha to "turk" it out to unwitting users.
After all, they've been using house numbers in Captcha for a while.

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cube00
It would have been good if the article explained the link between this work
and house numbers appearing in reCaptcha. Training the network perhaps?

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pervycreeper
Personal experience suggests that they are also using CAPTCHAs for the same
purpose. I wonder how that figures in to the project.

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13
Perhaps it doesn't, they might have just used that as a source for "things we
know are too hard for everybody but us". They weren't presenting houses with
dummy words like they were for book solves, so it seems unlikely they were
using it to train with unwitting human inputs.

~~~
learnstats2
I didn't appreciate doing this work for Google so I gave the wrong answer
every time. It passed me through the captcha every time.

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cube00
If it's choice between doing work for Google and solving those meaningless
random ones that that contribute to nothing I'll help Google anyday.

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learnstats2
That's a false choice.

The real choice is between doing Captcha work for Google and not doing
Captchas.

In this respect, you're welcome.

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softdev12
The article just skims over this part:

"To start off with, Goodfellow and co place some limits on the task at hand to
keep it as simple as possible. For example, they assume that the building
number has already been spotted and the image cropped so that the number is at
least one-third the width of the resulting frame. They also assume that the
number is no more than five digits long, a reasonable assumption in most parts
of the world."

This seems like a huge task. Someone has to go through all the thousands of
images and first crop them? During that time, it would seem like they could
just input the number into a database.

Maybe I'm missing something, but I read the "cracked" part to be a totally
automated system that scans all the pictures and pulls the numbers with no
human manipulation.

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sanxiyn
Of course cropping is also automated, but using the different algorithm.

Text detection and text recognition is a different problem. Text detection is
usually solved by stroke width transform. The article focuses on text
recognition using the neural network.

