🖊️✍️ 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 to download

📚 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.

🔍 Use Cases

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

📈 Performance

🔍 Accuracy: ~99% on the MNIST dataset.

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

🛠️ Technical Details

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

📥 Input Format

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

📤 Output Format

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

🧩 Model Training

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

💡 How to Use

Preprocess the Image: Resize and normalize the image to 28x28 pixels with values between 0 and 1.
Feed the Image: Input the preprocessed image into the model.
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.

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.

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.

# 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.

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}')

⚠️ Limitations

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

👥 Contributors

Developer: Lizardwine ([email protected])
Organization: lizardwine
Date: 06/06/2024

📝 References

MNIST Dataset: Link
CNN Architecture: Link

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