Document OCR for Node.js with GPT Vision

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GPT Vision is probably the best generic OCR and document parsing tool out there. It can handle invoices, contracts, receipts, and even hand written notes. However, there are some limitations to the API that make it hard to work with: 1) No support for JSON output 2) No support for PDFs llm-document-ocr is a simple Node library that does these pre and post processing steps for you. It converts PDFs into PNGs, crops whitespace around the images, and parses the JSON output. Hope this saves you some time if you are building your own OCR stack on top of GPT and other LLMs!

Visit product View on Hacker NewsNov 16, 2023

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

64%64% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% 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

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

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

40%40% 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

14%14% 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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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