All text in Brooklyn
brooklyn.alltexts.nyc
brooklyn.alltexts.nyc
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with referer: https://www.alltext.nyc/it's short, phonetic, includes "words" (plural), homonyms for "word scene", literally describes the collection of each word seen in public scenery, etc.
Arguably wordscene.nyc also.
For those who don't know, Chabad, an orthodox Jewish organization, has a large promotional presence in Brooklyn
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!
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!
"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
Either way, really cool project.
[1]: https://en.wikipedia.org/wiki/Salience_(neuroscience)#Salien...
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].
[0]https://www.alltext.nyc/?search=fart [1]https://www.alltext.nyc/?search=blob+dylan
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.
the goal was to guess the location as quickly as possible
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).