🖊️✍️ 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.
1from keras.models import load_model
2
3# Load the pre-trained model
4model = load_model('path/to/DigitClassifier.keras')
Preprocessing the Input
Preprocess the input image to fit the model's requirements.
1import numpy as np
2from keras.preprocessing import image
3
4def preprocess_image(img_path):
5 # Load the image
6 img = image.load_img(img_path, color_mode='grayscale', target_size=(28, 28))
7 # Convert to numpy array
8 img_array = image.img_to_array(img)
9 # Normalize the image
10 img_array = img_array / 255.0
11 # Reshape to add batch dimension
12 img_array = np.expand_dims(img_array, axis=0)
13 return img_array
14
15# Example usage
16img_path = 'path/to/your/image.png'
17processed_image = preprocess_image(img_path)
Making Predictions
Use the model to predict the digit from the processed image.
1# Predict the digit
2predictions = model.predict(processed_image)
3
4# Get the digit with the highest probability
5predicted_digit = np.argmax(predictions)
6print(f'The predicted digit is: {predicted_digit}')
Full Example
Combining all steps into a single example.
1from keras.models import load_model
2from keras.preprocessing import image
3import numpy as np
4
5# Load the pre-trained model
6model = load_model('path/to/DigitClassifier.keras')
7
8def preprocess_image(img_path):
9 img = image.load_img(img_path, color_mode='grayscale', target_size=(28, 28))
10 img_array = image.img_to_array(img)
11 img_array = img_array / 255.0
12 img_array = np.expand_dims(img_array, axis=0)
13 return img_array
14
15img_path = 'path/to/your/image.png'
16processed_image = preprocess_image(img_path)
17
18predictions = model.predict(processed_image)
19predicted_digit = np.argmax(predictions)
20print(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 (x@lizardwine.com)
- Organization: lizardwine
- Date: 06/06/2024
📝 References
- MNIST Dataset: Link
- CNN Architecture: Link
🎉 Thank you for using our Handwritten Digit Recognition Model! 🎉
