Unscribbler – Simple Handwriting Reader

Hacker News

This is a handwriting-to-text converter! Just follow the instructions on the page and you're good to go :) Background: I've been tutoring on the side for a while and it's apparent that the whole process can be smoothed out, with the end goal being an AI tutor buddy with a stylus interface. This is a little step in that direction. As for implementation details, I forked excalidraw (at https://github.com/excalidraw ), got a gcp free tier instance running, and scraped together a Google K8s Engine cluster serving with torchserve. Luckily there's a great deal on the public preview of c3 cpus at the moment. For the model, I'm using trocr-base-handwritten ( https://huggingface.co/microsoft/trocr-base-handwritten ). Let me know if anyone has any ideas, suggestions, and/or tips!

Visit product View on Hacker NewsMar 22, 2023

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17points

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, using · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

31%31% 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.

Correct prediction on native model

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