MNIST Digit Recognition
MNIST Digit Recognition
Class-wise F1-score Distribution
Mnist Digit Recognition Home Page
Mnist Digit Recognition Hand Drawn Digit
Mnist Digit Recognition Prediction
Performance Across Different Models
Heatmap of classwise F1-score
Delta from best classwise F1-score
Class-wise F1-score Distribution
Mnist Digit Recognition Home Page
Project Description
An interactive web application where users can draw handwritten digits and get real-time predictions from a trained ML model using the MNIST dataset.
Responsibilites
Developed an end-to-end handwritten digit recognition system using the MNIST dataset.
Achieved 98.16% accuracy and a 0.98 weighted F1-score using a Support Vector Classifier (SVC).
Built a FastAPI backend to serve real-time predictions from the trained ML model.
Designed an interactive Next.js frontend where users can draw digits and see predictions instantly.
Hosted the application using Firebase App Hosting with model inference served via Firebase Cloud Functions.
Explored multiple ML models (Logistic Regression, SVC, Random Forest, KNN) and optimized for best performance.
Debugged and resolved issues related to preprocessing pipelines and production model deployment, ensuring a smooth ML workflow.