Title: Handwritten Digit Recognition System Using TensorFlow, Keras, and OpenCV Neural Networks
Workflow -
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Data Preparation and Preprocessing:
- Load the MNIST dataset containing handwritten digit samples.
- Split the dataset into training and testing sets.
- Normalize the pixel values of the images to a range between 0 and 1 to enhance model performance and convergence during training.
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Model Architecture Design:
- Create a neural network model using TensorFlow and Keras.
- Design the model architecture:
- Include a flattened input layer to accept the pixel values of the digit images.
- Add two dense hidden layers with ReLU activation functions to capture complex patterns in the data.
- Incorporate a dense output layer with softmax activation to classify the digits into ten categories (0 to 9).
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Model Training and Evaluation:
- Compile the model, specifying the optimizer as 'adam' and the loss function as 'sparse_categorical_crossentropy'.
- Train the model on the training dataset for a predefined number of epochs.
- Evaluate the trained model's performance on the testing dataset to calculate metrics such as loss and accuracy.
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Model Saving and Loading:
- Implement functionality to save the trained model to disk as 'handwritten_digits.model' for future use.
- Provide the option to load the pre-trained model from disk if available, allowing for inference on new data without retraining.
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Custom Image Prediction:
- Develop functionality to load custom images containing handwritten digits.
- Preprocess the custom images to ensure compatibility with the model input format.
- Utilize the trained model to predict the digit represented by each custom image.
- Visualize the predicted digit alongside the original image using matplotlib.
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Error Handling and Robustness:
- Implement robust error handling mechanisms to manage exceptions during image loading, preprocessing, and prediction.
- Ensure the system's reliability by addressing potential issues such as incorrect image paths or corrupted image files.