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https://github.com/amiriiw/digit_classification
Welcome to the Digit Classification Project! This project focuses on training a model to classify handwritten digits and using the trained model to predict digits from new images.
https://github.com/amiriiw/digit_classification
digit-classification keras mysql mysql-database numpy os sklearn tensorflow
Last synced: 5 days ago
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Welcome to the Digit Classification Project! This project focuses on training a model to classify handwritten digits and using the trained model to predict digits from new images.
- Host: GitHub
- URL: https://github.com/amiriiw/digit_classification
- Owner: amiriiw
- License: mit
- Created: 2024-08-12T14:40:32.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-06T14:22:05.000Z (about 1 month ago)
- Last Synced: 2024-10-17T13:06:00.762Z (22 days ago)
- Topics: digit-classification, keras, mysql, mysql-database, numpy, os, sklearn, tensorflow
- Language: Python
- Homepage:
- Size: 44.9 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Digit Classification Project
Welcome to the **Digit Classification Project**! This project focuses on training a model to classify handwritten digits and using the trained model to predict digits from new images.
```plaintext
Digit classification/
├── digit_classification_model_trainer.py
├── digit_classification_detector.py
├── requirements.txt
```
## Overview
This project consists of two main components:
1. **digit_classification_model_trainer.py**: This script is responsible for training a model to classify digits based on a dataset of handwritten digits.
2. **digit_classification_detector.py**: This script uses the trained model to predict digits from new images.
## Libraries Used
The following libraries are used in this project:
- **[tensorflow](https://www.tensorflow.org/)**: TensorFlow is an open-source machine learning library used for training and inference in this project.
- **[numpy](https://numpy.org/)**: NumPy is used for numerical operations on image data.
- **[sklearn](https://scikit-learn.org/)**: Scikit-learn is used for splitting the dataset into training and testing sets.
- **[os](https://python.readthedocs.io/en/stable/library/os.html)**: The OS module is used for directory and file manipulation.
- **[mysql-connector](https://pypi.org/project/mysql-connector-python/)**: This library is used for connecting to a MySQL database to store classification results.
## Detailed Explanation
### `digit_classification_model_trainer.py`
This script is essential for training the digit classification model. The key components of the script are:
- **DigitClassifier Class**: This class manages the entire training process, from loading and preprocessing the dataset to building, training, and saving the model. It uses TensorFlow’s Keras API to create a simple neural network for digit classification.
- **_load_and_preprocess_data() Method**: This method loads the images from the dataset directory, preprocesses them (resizes and normalizes), and splits them into training and testing sets.
- **_build_model() Method**: This method constructs a simple neural network model with a flatten layer, a dense hidden layer, and an output layer for digit classification.
- **train() Method**: This method trains the model on the preprocessed dataset.
- **evaluate() Method**: This method evaluates the model on the test dataset and prints the test accuracy.
- **save_model() Method**: This method saves the trained model to a file for later use.
### `digit_classification_detector.py`
This script uses the trained model to predict digits from new images. The key components of the script are:
- **DigitPredictor Class**: This class loads the trained model and provides methods to preprocess images and make predictions.
- **preprocess_image() Method**: This method loads and preprocesses a new image, preparing it for prediction.
- **predict() Method**: This method predicts the digit in the provided image using the trained model and prints the predicted digit and confidence.
- **save_to_database() Method**: This method saves the prediction results to a MySQL database.
- **classify_images() Method**: This method classifies images from a given directory or a single image.
### How It Works
1. **Model Training**:
- The `digit_classification_model_trainer.py` script reads images from the specified dataset directory.
- The images are resized, normalized, and fed into a neural network model, which is trained to classify them into one of ten digit classes (0-9).
- The trained model is saved for later use.
2. **Digit Prediction**:
- The `digit_classification_detector.py` script loads the trained model and processes new images.
- Each image is preprocessed and classified by the model, which outputs the predicted digit and confidence.
## Dataset
The dataset used for training the model can be accessed via this [Dataset](https://drive.google.com/drive/folders/127c2JXxepw8iQ6gRRinPIM5fnRY4ztS9?usp=sharing).
## Installation and Setup
To use this project, follow these steps:
### 1. Clone the repository:
```bash
git clone https://github.com/amiriiw/digit_classification
cd digit_classification
cd Digit/ classification
cd Digit/ classification/ project
```
### 2. Install the required libraries:
```bash
pip install -r requirements.txt
```
### 3. Setup the database:
You need to create a MySQL database and configure the connection settings.
1. **Install MySQL**: Make sure MySQL is installed and running on your system. You can download it from [here](https://dev.mysql.com/downloads/installer/).
2. **Create a new MySQL database**:
- Open your MySQL shell or MySQL Workbench and run the following commands to create a new database:
```sql
CREATE DATABASE digit_classification;
```
3. **Configure your MySQL connection**:
- The script `digit_classification_detector.py` is configured to connect to a MySQL database using the following settings:
```python
host='localhost',
user='username',
password='password',
database='digit_classification'
```
- Update these values in the script if your MySQL settings are different (e.g., different username, password, or database name).
4. **Run the prediction script**: The first time you run the script, it will automatically create the necessary tables in the database.
```bash
python digit_classification_detector.py
```
### 4. Prepare the dataset:
Download and prepare your dataset, ensuring it's structured appropriately for training (folders named `0` to `9`, each containing respective digit images).
### 5. Run the model training script:
```bash
python digit_classification_model_trainer.py
```
### 6. Use the trained model to predict digits from new images:
```bash
python digit_classification_detector.py
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.