Educational Implementation · JavaScript
ocr-livive-net
Browser-native OCR from scratch in WebAssembly. Four transparent classifiers (Centroid, k-NN, Perceptron, hand-written MLP), zero ML dependencies, and paper-and-pencil auditable arithmetic.
What it is
This repository implements four machine learning classifiers (Centroid, k-NN, Perceptron, and a hand-written MLP) from scratch in Rust for browser-based OCR via WebAssembly, emphasizing transparency and zero dependencies. It includes data preparation scripts, training and evaluation code, unit tests, and a web demo.
Where it counts
- Machine Learning85%
- Computer Vision85%
- Data Science15%
- MLOps10%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
Around it on the Machine Learning board
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- 06, shared rankmines-smLibrary / Framework · Rust64.6AdvancedJarad PeckenpaughMachine Learning
- 08ocr-livive-netEducational Implementation · JavaScript63.4AdvancedJarad PeckenpaughMachine Learning
- 09, shared rankmamba-viewAI Developer Tool · Python62.2AdvancedRickMachine Learning
- 09, shared rankResNet_Family-from-scratchArchitecture Reimplementation · Jupyter Notebook62.2AdvancedPop AlexandruMachine Learning
