Search all text in New York City

Overall reception and uses

• Many commenters find the project delightful and “exceedingly fun,” describing it as something they could spend hours exploring.
• People immediately use it to find personal landmarks (e.g., childhood bagel shops) and local culture (graffiti writers, stickers, slogans, political posters).
• Some note its value for OSINT and imagine that intelligence agencies likely have similar tools at global scale.

Playing with the search

• Users test funny or crude words (“fart,” “pedo,” “sex,” “foo,” “fool”), getting amusing misreads and coining it as a kind of game.
• Another game emerges: find real English words with the fewest hits; examples like “scintillating,” “calisthenics,” “perplexed,” “Buxom,” etc.
• People search for graffiti tags, politicians’ names, slogans, and niche phrases to probe cultural traces across the city.
• Food terms (“bagels,” “pizza,” “sushi,” “hotdog,” “massage”) reveal dense and uneven spatial distributions; one person notes sushi is heavily Manhattan‑centric.

OCR quality and quirks

• Multiple comments say the idea is brilliant but current OCR accuracy is “pretty bad” for many queries.
• Misreads of Google watermarks, cropped signs, and partial words generate large numbers of false positives.
• Some searches work well for clear signage; others show systematic errors: “OPEN” → “OBEY,” “food” → “foo,” and numerous comical reinterpretations.

Technical and cost considerations

• Commenters estimate OCR compute as manageable on consumer hardware, but highlight Google Maps / Street View API costs (tens of thousands of dollars at list prices) as the real barrier.
• Discussion notes ~8 million panoramas processed; various back‑of‑the‑envelope calculations of image throughput and API fees appear.
• A linked talk suggests the creator used publicly-available Street View imagery and macOS’s built-in OCR via Shortcuts, possibly without paid API access; it’s unclear how rate limits were handled.

Related projects and desired extensions

• Links to similar efforts: earlier Brooklyn‑only and London versions, a New York traffic‑camera semantic search project, and a UK building‑safety use of Street View.
• Several people want an API, deduplication of near-identical views, CLIP/semantic image embeddings, or a “text‑only Street View.”
• Others imagine this as a Google Maps layer for discovering niche businesses by sign text.

Data freshness, filtering, and tangents

• Some try to infer the capture timeframe from protest posters and political signs.
• There’s curiosity about why some official notices or offensive words are hard to find and speculation around mild censoring in the write‑up’s links.
• One tangent raises the lack of simple, accessible text‑to‑speech tools for blind users; replies point to cost and existing assistive tech rather than this project specifically.