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license: apache-2.0
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# 🖊️✍️ Handwritten Digit Recognition Model

## 📄 Overview

🤖 **Model Name:** Handwritten Digit Recognition Model

🧠 **Model Type:** Convolutional Neural Network (CNN)

📊 **Input:** 28x28 grayscale images of handwritten digits (0-9)

🔢 **Output:** A 10-dimensional vector representing the probabilities of each digit (0-9)

🎯 **Purpose:** To classify handwritten digits from images with high accuracy

☁️ **Download:** Click [here](https://huggingface.co/lizardwine/DigitClassifier/resolve/main/DigitClassifier.keras?download=true) to download

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## 📚 Description

This model is designed to recognize handwritten digits from 0 to 9. It processes input images of size 28x28 pixels and outputs a vector of 10 probabilities, each corresponding to one of the digits. The digit with the highest probability is selected as the predicted class.

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## 🔍 Use Cases

1. **Educational Tools:** 🏫 Helping students learn and practice handwriting recognition.
2. **Digitization Projects:** 📄 Converting handwritten documents into digital format.
3. **Assistive Technology:** 🦾 Assisting individuals with disabilities in digit writing.

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## 📈 Performance

🔍 **Accuracy:** ~99% on the MNIST dataset.

🕒 **Latency:** Fast inference time suitable for real-time applications.

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## 🛠️ Technical Details

- **Architecture:** Convolutional Neural Network (CNN)
- **Layers:** Convolutional layers, pooling layers, fully connected layers
- **Activation Functions:** ReLU, Softmax

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## 📥 Input Format

- **Type:** Grayscale image
- **Shape:** 28x28 pixels
- **Range:** 0-1 (pixel intensity)

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## 📤 Output Format

- **Type:** Probability vector
- **Shape:** 10-dimensional
- **Range:** 0-1 (sum of probabilities equals 1)

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## 🧩 Model Training

- **Dataset:** MNIST dataset 📚
- **Training Epochs:** 10
- **Batch Size:** 32
- **Optimizer:** Adam
- **Learning rate:** 1e-3

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## 💡 How to Use

1. **Preprocess the Image:** Resize and normalize the image to 28x28 pixels with values between 0 and 1.
2. **Feed the Image:** Input the preprocessed image into the model.
3. **Interpret the Output:** Analyze the 10-dimensional output vector to find the digit with the highest probability.
### Loading the Model

To use the model, first, load it using Keras.

```python
from keras.models import load_model

# Load the pre-trained model
model = load_model('path/to/DigitClassifier.keras')
```

### Preprocessing the Input

Preprocess the input image to fit the model's requirements.

```python
import numpy as np
from keras.preprocessing import image

def preprocess_image(img_path):
    # Load the image
    img = image.load_img(img_path, color_mode='grayscale', target_size=(28, 28))
    # Convert to numpy array
    img_array = image.img_to_array(img)
    # Normalize the image
    img_array = img_array / 255.0
    # Reshape to add batch dimension
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

# Example usage
img_path = 'path/to/your/image.png'
processed_image = preprocess_image(img_path)
```

### Making Predictions

Use the model to predict the digit from the processed image.

```python
# Predict the digit
predictions = model.predict(processed_image)

# Get the digit with the highest probability
predicted_digit = np.argmax(predictions)
print(f'The predicted digit is: {predicted_digit}')
```

### Full Example

Combining all steps into a single example.

```python
from keras.models import load_model
from keras.preprocessing import image
import numpy as np

# Load the pre-trained model
model = load_model('path/to/DigitClassifier.keras')

def preprocess_image(img_path):
    img = image.load_img(img_path, color_mode='grayscale', target_size=(28, 28))
    img_array = image.img_to_array(img)
    img_array = img_array / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

img_path = 'path/to/your/image.png'
processed_image = preprocess_image(img_path)

predictions = model.predict(processed_image)
predicted_digit = np.argmax(predictions)
print(f'The predicted digit is: {predicted_digit}')
```

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## ⚠️ Limitations

- **Handwriting Variability:** Performance may decrease with highly unconventional handwriting.
- **Noise:** Model performance can be affected by noisy or poor-quality images.

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## 👥 Contributors

- **Developer:** Lizardwine (x@lizardwine.com)
- **Organization:** lizardwine
- **Date:** 06/06/2024

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## 📝 References

- MNIST Dataset: [Link](http://yann.lecun.com/exdb/mnist/)
- CNN Architecture: [Link](https://en.wikipedia.org/wiki/Convolutional_neural_network)

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🎉 **Thank you for using our Handwritten Digit Recognition Model!** 🎉