| --- |
| license: apache-2.0 |
| --- |
| # 🖊️✍️ 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 |
|
|
| --- |
|
|
| ## 📚 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 |
|
|
| 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. |
|
|
| --- |
|
|
| ## 📈 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 |
|
|
| 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. |
|
|
| --- |
|
|
| ## ⚠️ 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](http://yann.lecun.com/exdb/mnist/) |
| - CNN Architecture: [Link](https://en.wikipedia.org/wiki/Convolutional_neural_network) |
|
|
| --- |
|
|
| 🎉 **Thank you for using our Handwritten Digit Recognition Model!** 🎉 |
