Portfolio Project

Handwriting Legibility Scoring

PyTorch CNN Fine-Tuning

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Machine Learning Python PyTorch AWS Docker CNN

Draw a digit (0-9) or load a sample, then score how legible it is.

  • Pick a sample from “Daniel’s Handwritten Numbers”, or draw a digit on the canvas.
  • Click “Rate digit” to submit.
  • Use the eraser to clear the canvas and try another digit.
  • Review the predicted digit, confidence, and legibility score breakdown.
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STAR Summary

Situation
My wife says my handwriting is hard to read. I wanted an objective score.
Task
Owned the end-to-end build, from implementation through the final deliverable.
Action
  • Built three digit-recognition models, moving from a simple baseline to a CNN.
  • Trained on MNIST (60,000 digits) and selected the best model.
  • Deployed it behind a serverless scoring API for the live demo.
Result
  • The best model reached ~99% test accuracy on MNIST (see notebook).
  • On a small personal handwriting set, accuracy was ~75.6% (not a benchmark; see notebook).
  • My wife was right.

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