"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.
It prevents any rework from having to renumber the buildings if things are being built up heavily over time. Kind of like an unsorted array vs a sorted array. The unsorted one is a pain to search, but you can just slap things on to end without a worry.
The sane solution for that is to just use "number of meters since start of street" as the building number. That way you can always fit new buildings between existing ones and get the bonus that if you are at building 100 and want to get to 200 you know that's 100 meters away. Usually you'd also do even numbers on one side of the street and odd on the other to make it easy to know which side to look at. This means you can't have doors closer than 2 meters apart which is usually ok. If you really need more doors then 100A and 100B are the usual hack.
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...
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.
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.
"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.
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.
"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.