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01Handwritten Digit Classifier

Overview

Designed and trained a modular Multi-Layer Perceptron (MLP) for classification using a filtered MNIST dataset. Built an interactive GUI supporting real-time digit drawing and confidence-based inference.

Key Features

  • Custom pipeline including cropping, centering, normalization, and aspect-ratio preservation
  • Interactive GUI for real-time digit drawing and inference
  • Integrated optimizers (SGD, Momentum, ADAM) to evaluate maximum accuracy
View Source Code

Architecture

DRAW
PREPROCESS
MLP MODEL
CONFIDENCE SCORE

Technologies

PyTorchNumPyTkinterMatplotlib
2026
System Online