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it's short, phonetic, includes "words" (plural), homonyms for "word scene", literally describes the collection of each word seen in public scenery, etc.
This is really cool, and hints at a near-future possibility of building a search engine on top of just about anything. It's clear we've moved past the ability to just search for website url's and webpage content. Anything that can be indexed - regardless of type of data or dimension (space, time, etc.) will be searchable.
There is much that isn't indexed here. "718" yields less than 4,000 hits, which seems very low. There are many old buildings in Brooklyn that still sport "fallout shelter" signs, but ATiB returns only 13 results for that term, with many duplicate images. Here's an example of a missing result, probably one of thousands: https://maps.app.goo.gl/AdsYFzf7MAZoo4Vc8
The OCR has a lot of false positives, though. "Truck" definitely was not what I was looking for, but it makes up a significant amount of the search. "culo" fuzzy/exact results were also surprisingly disappointing :) .
If there's a way to change the text-matching accuracy and add this filter to the front-end, I'd be lost here forever. Switching locations would also be a fun way to scale this up. Throw an Adsense add on there and you're looking at a decent passive income!
It’s been kind of useless honestly. The city allowed the registrars to reserve many domains as “premium” domains, i.e. almost all neighborhoods were reserved with prices in the tens of thousands of dollars. Initially you had to specify a NYC postal address, no PO boxes, allegedly to prevent domains from being scooped up by non-NYC entities. And there was no privacy protection (that seems to have changed).
GoDaddy is the registry, but not the sole registrar that it can be registered through. Both NameCheap and Gandi are options - and I am sure others too.
NB: I work for GoDaddy, but am not responding in any capacity on their behalf.
I wanted to use a word that I figured would be rarely seen in Brooklyn, so I tried: “Gripe”
The correct identifications center around “Vacuna de la gripe” (flu vaccine).
The remainder are all mismatches, such as “Grape” at a very sharp angle. Funnily, the majority of the mislabeled samples are all due to the “Good Grips” brand logo. This logo has a small underlined “s” at the end that looks like a “E” when you squint at the JPEG. I’ll give the OCR model a pass on this one!
I've been either stunned, or disappointed depending on the word.
"hello" gives four images of the same building with "hello" clearly written, as well as a few images of "hello" grafiti. Impressed
"table" gives six results- four of which are clearly pictures of either leaves or the sky. Two are blurry buildings, but I cant seem to find the text "table"... it could be there though? Not impressed
"car" gives Six unique results, some of which "car" is the prefix of a word. Impressed
I feel like there's a bias for this, where you likely wouldn't register seeing a mundane number a bunch (like say, 378), but 666 and 420 would stick out even once, so you'll remember all the other times you've seen them. From googling though, I'm having trouble finding a name for this that I'm familiar with; the best I can find seems to be salience bias[1], but I'm not sure if that's what I was thinking of; I've certainly never heard of it before.
The first thing I searched for was my favorite graffiti[0]. I always see it while walking the dog. It brought up a lot of false positives like 'mart' and 'part' if they were at an angle or partially obscured.
I also tried searching for Blob Dylan since there always seems to be a bunch of those around, and it only brought up 2–3 results[1].
This would be really useful in GeoLocation GeoGuessing games - and in order to ID a location based on any limited text you can discern. Wonder how hard it would be to apply it to other locations.
Geoguessr. Its popularity has only been on a steady rise to this day.
For those curious, there are different modes, including the one where you cannot move along the street at all (but you can look around by turning in that same stationary spot).