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.

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.

The following libraries are used in this project:

• tensorflow: TensorFlow is an open-source machine learning library used for training and inference in this project.
• numpy: NumPy is used for numerical operations on image data.
• sklearn: Scikit-learn is used for splitting the dataset into training and testing sets.
• os: The OS module is used for directory and file manipulation.

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.

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.

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.

The dataset used for training the model can be accessed via this Dataset.

To use this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/amiriiw/digit_classification
   cd digit_classification

2. Install the required libraries:

   pip install tensorflow numpy scikit-learn

3. Download and prepare your dataset, ensuring it's structured appropriately for training.

4. Run the model training script:

   python digit_classification_model_trainer.py

5. Use the trained model to predict digits from new images:

   python digit_classification_detector.py

This project is licensed under the MIT License. See the LICENSE file for details.