README.md

metadata

title: Image-Digit-Classification
emoji: 🖋️
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 6.6.0
app_file: app.py
pinned: false

Digit Classification System

This project is a machine learning application designed to recognize handwritten digits using Convolutional Neural Networks (CNN). It includes tools for data preprocessing, model training, and an interactive drawing interface for real-time predictions.

👉 Live Demo

Files

• app.py: The main application file that creates a Gradio interface for the digit recognition system.
• main_analysis.ipynb: A Jupyter notebook that contains the data processing, model training, and evaluation code.
• train_mnist.csv: The dataset used for training the model.

Data source

• Source: MNIST in CSV (Kaggle)
• Format: CSV containing grayscale pixel values (0–255).
• Target: The label column representing the digit (0–9).
• Dimensions: Aside from the label, each row consists of 784 features (a flattened 28×28 pixel image).

Key Features

• Data processing for model training: Processes raw CSV data into $28 \times 28$ grayscale images by parsing pixel coordinates (from train_mnist.csv).
• Dynamic Data Augmentation (new): Enhances model generalization using random rotations, translations, and zooms.
• Automated Hyperparameter Tuning (new): Utilizes BayesianOptimization to search for the best architecture, including the number of layers, filters, dropout rates, and optimizers.
• Comprehensive Evaluation: Features detailed visualization of label distribution, confusion matrices, and an analysis of misclassified samples.
• Interactive Demo: A Gradio interface that allows users to draw digits and get instant predictions from the trained model.

Tech Stack

• Core: Python, TensorFlow/Keras
• Tuning: Keras Tuner
• Data Science: Pandas, NumPy, Scikit-learn
• Visualization: Matplotlib, Tkinter, Pillow (PIL)
• UI: Gradio

Results

Based on the tuning process, the model typically achieves >98% accuracy on the test set.