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Adventures in OCR

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Adventures in OCR

Hello HN! In a recent "Ask HN: What are you working on?" thread, I mentioned I was working on OCRing a large book: https://news.ycombinator.com/item?id=41971614 The post generated some interest so I thought I would keep HN posted. The book is Saint-Simon’s Memoirs -- an invaluable historical account of the French court under Louis XIV, full of wit, sharp observations, and of incredible literary value. I'm OCRing the edition of reference made between 1879-1930, that contains a lot of comments and footnotes: 45 volumes, ~27,000 pages. Here's a link to a blog post that describes the techniques used so far (the project is still ongoing): https://blog.medusis.com/38_Adventures+in+OCR.html But you may also directly access the result here: https://divers.medusis.net/boislisle/pub This web app (not optimized for mobile, sorry) solves a tricky problem of preloading images efficiently. In short: preloading the next image isn't enough, since browsers will repaint if an image is moved, or scaled. Or browsers won't paint at all if visibility is hidden or opacity is zero, and will paint only when those values change. On an average, slow machine, this takes visible time. But if an image is simply behind another element, it will be painted, and the removal of the covering element or changing the z-index will not trigger a repaint. (Preloading is important because it lets one review results fast; if one has to wait 150-200 ms between images it's simply discouraging). Would love to hear feedback; happy to answer any question!

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
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best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: lua, 000, io · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, new, notes · Missing: agents, macos, agent
34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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