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| 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](https://huggingface.co/spaces/Arcus72/Image-Digit-Classification) | |
| ## 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)](https://www.kaggle.com/datasets/oddrationale/mnist-in-csv) | |
| - **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. | |