pXY.js - pixel analysis for HTML5 Canvas

Hacker News

This lib provides a pleasant interface for analyzing <canvas> pixels in an effort to speed up implementation, readability and debugging of custom analysis algorithms such as document feature extraction, OCR segmentation, etc. Why? I needed an interactive tool that would assist (a team of monkeys) in recognizing a document structure from rasterized or scanned PDF forms because automated tools like Omnipage and Finereader offer no efficient method of form field data-binding, auto field naming, schema mapping and so forth. While the actual algorithms and the tool is closed-source for now, pXY.js was written to serve as the core. tute: http://o-0.me/pXY/ repo: https://github.com/leeoniya/pXY.js

Visit product View on Hacker NewsOct 1, 2012

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

13%13% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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