https://github.com/asrot0/handdrawndigitai
🚀AI-powered app for handwritten digit recognition with an interactive GUI built 🎨 using CNN and CustomTkinter.
https://github.com/asrot0/handdrawndigitai
artificial-intelligence cnn deep-learning digit-recognition interactive-drawing machine-learning mnist tensorflow
Last synced: 28 days ago
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🚀AI-powered app for handwritten digit recognition with an interactive GUI built 🎨 using CNN and CustomTkinter.
- Host: GitHub
- URL: https://github.com/asrot0/handdrawndigitai
- Owner: asRot0
- Created: 2024-11-21T12:00:33.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-03-03T09:16:45.000Z (about 2 months ago)
- Last Synced: 2025-04-03T15:12:59.828Z (28 days ago)
- Topics: artificial-intelligence, cnn, deep-learning, digit-recognition, interactive-drawing, machine-learning, mnist, tensorflow
- Language: Python
- Homepage:
- Size: 7.64 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎨 HandDrawnDigitAI
Welcome to **HandDrawnDigitAI** 🤖 – an intuitive app for recognizing hand-drawn digits! Powered by **CustomTkinter**, a pre-trained **Convolutional Neural Network (CNN)**, and a user-friendly **GUI**, this project brings machine learning right to your fingertips. Draw digits, recognize them instantly, and enjoy the seamless experience!
---
## 🗂️ Project Structure
```plaintext
HandDrawnDigitAI/
│
├── app/
│ ├── __init__.py # Makes `app` a package
│ ├── gui.py # Contains the GUI logic
│ ├── digit_recognizer.py # Logic for recognizing digits
│ ├── themes.py # Themes dictionary
│ └── utils.py # Helper utility functions
│
├── models/
│ └── cnn_model.h5 # Pre-trained model
│
├── logs/ # Stores temporary image files
│
├── notebooks/
│ └── cnn_mnist.ipynb # Notebook for model
│
├── run.py # Main entry point to the app
├── requirements.txt # Dependencies for the project
└── README.md # Documentation
```
---
## 🚀 Features
- 🖼️ **Interactive Drawing Canvas**
Use the mouse to draw digits directly on the canvas.
- 🧠 **AI-Powered Digit Recognition**
Recognize digits (0-9) using a **pre-trained CNN model** trained on the **MNIST dataset**.
- 🎨 **Dynamic Themes**
Choose from multiple visually appealing themes:
🔵 *Oceanic* | 🌙 *Dark Mode* | 🎨 *Vibrant* | 🖤 *Corporate* | 🌸 *Pink Black*
- 🛠️ **Modular Codebase**
Clean and organized project structure for easy navigation.
---
## 🎯 How It Works
1. **Draw a digit** on the canvas.
2. **Press the "Predict" button**, and the model will recognize the digit.
3. If the prediction isn't clear, **adjust and re-draw** on the canvas!
4. Use the **"Clear Canvas" button** to start over.
---
## 📋 Requirements
Make sure you have the following dependencies installed before running the app:
```plaintext
tensorflow==2.10.0
customtkinter==5.1.2
pillow==9.2.0
numpy==1.23.0
opencv-python==4.6.0
```
#### Install them using:
```
pip install -r requirements.txt
```
---
## 📂 Files & Directories
### 📁 app/
- **gui.py**
Handles the graphical user interface logic, including button interactions and canvas drawing.
- **digit_recognizer.py**
Preprocesses the canvas image and uses the CNN model for predictions.
- **themes.py**
Stores theme configurations for a visually appealing UI.
- **utils.py**
Helper functions to support digit recognition and GUI.
### 📁 models/
- **cnn_model.h5**
Pre-trained CNN model saved in Keras HDF5 format.
### 📁 logs/
- Temporary directory to store canvas snapshots during runtime.
### 📁 notebooks/
- **cnn_mnist.ipynb**
A Jupyter Notebook used to train the CNN model on the MNIST dataset.
### `run.py`
- The main entry point to launch the application.
---
## 🖥️ Running the Application
Follow these steps to get started:
1. Clone the repository:
```
git clone https://github.com/asRot0/HandDrawnDigitAI.git
cd HandDrawnDigitAI
```
2. Install the required dependencies:
```
pip install -r requirements.txt
```
3. Run the app:
```
python run.py
```
4. Draw digits and enjoy the magic! ✨
---
## 🎨 Themes Preview
| 🌙 Dark Mode | 🔵 Oceanic | 🌸 Pink Black |
|:------------:|:----------:|:-------------:|
|  |  |  |
### 🌸 *`BlackPink`* Theme
---
## 🔢 Key Math Concepts for CNN & MNIST
Here are some important mathematical formulas used in this project
### **1️⃣ Convolution Operation (Feature Extraction in CNN)**
Convolutional layers apply filters to extract features from input images.
$$
\LARGE O(i, j) = \sum_m \sum_n I(i+m, j+n) \cdot K(m, n)
$$
- $`O(i, j)`$ → Output feature map at position $(i, j)$.
- $`I(i+m, j+n)`$ → Input image pixels affected by the filter.
- $`K(m, n)`$ → Kernel (`filter`) values applied to the input.
📌 **Why Add?**
- Represents how a **filter (kernel) slides over an image** to extract meaningful features.
- Core operation in **Convolutional Neural Networks (CNNs)**.
---
### **2️⃣ ReLU Activation Function (Hidden Layers)**
ReLU introduces non-linearity to the model by keeping only positive values.
$$
\LARGE \text{ReLU}(x) = \max(0, x)
$$
- $`x`$ → Input value to the activation function.
📌 **Why Add?**
- Helps **prevent vanishing gradients**.
- Improves CNN’s ability to learn complex patterns.
---
### **3️⃣ Max Pooling (Dimensionality Reduction)**
Max pooling reduces the spatial size of feature maps while preserving key information.
$$
\LARGE P(i, j) = \max_{(m,n) \in R} F(i+m, j+n)
$$
- $`P(i, j)`$ → Pooled output value at position $(i, j)$.
- $`F(i+m, j+n)`$ → Input feature map values in the pooling region.
- $`R`$ → Pooling region (e.g., 2×2 or 3×3 window).
📌 **Why Add?**
- Reduces computation and prevents **overfitting**.
- Keeps **dominant features** while discarding unnecessary details.
---
### **4️⃣ Softmax Function (Final Layer for Classification)**
The softmax function converts model outputs into probability distributions.
$$
\LARGE \text{Softmax}(z_i) = \frac{e^{z_i}}{\sum_{j=1}^{n} e^{z_j}}
$$
- $`z_i`$ → Raw score (logit) for class $( i )$.
- $`e^{z_i}`$ → Exponential of the logit, ensuring positive values.
- $`\sum_{j=1}^{n} e^{z_j}`$ → Sum of exponentials across all $( n )$ classes (normalization factor).
📌 **Why Add?**
- Softmax assigns **probabilities to digit classes (0-9)**.
- Ensures outputs sum up to **1**, making it interpretable.
---
### **5️⃣ Cross-Entropy Loss (Training the CNN)**
Cross-entropy measures the difference between predicted and actual labels.
$$
\LARGE \mathcal{L} = -\sum_{i=1}^{n} y_i \log(\hat{y_i})
$$
- $`\mathcal{L}`$ → Cross-entropy loss value.
- $`y_i`$ → Actual label (ground truth) for class $( i )$ (1 for correct class, 0 otherwise).
- $`\hat{y_i}`$ → Predicted probability from the `softmax` function.
📌 **Why Add?**
- Penalizes incorrect predictions by increasing the loss.
- Common **loss function** for **multi-class classification**.
---
### **6️⃣ Adam Optimizer (Gradient Descent for CNN Training)**
Adam adjusts learning rates based on gradients to optimize CNN performance.
$$
\LARGE \theta_{t+1} = \theta_t - \frac{\eta}{\sqrt{v_t} + \epsilon} m_t
$$
- $`\theta_t`$ → Model parameters at step $( t )$.
- $`m_t`$ → First moment estimate (mean of gradients).
- $`v_t`$ → Second moment estimate (variance of gradients).
- $`\eta`$ → Learning rate (step size).
- $`\epsilon`$ → Small constant to avoid division by zero.
📌 **Why Add?**
- Used as the **optimizer** in this project (`optimizer='adam'`).
- Combines **momentum & adaptive learning rates** for faster convergence.
These mathematical concepts power the **CNN architecture** used in this project. They help in **feature extraction, classification, optimization, and learning** to recognize hand-drawn digits **efficiently and accurately**.
---
## 📖 Model Training (Optional)
If you'd like to retrain the model:
1. Open the Jupyter Notebook in the `notebooks`/ directory.
2. Train the CNN on the MNIST dataset.
3. Save the updated model as cnn_model.h5 in the models/ directory.
## 💡 Future Enhancements
- ✍️ Add support for multiple languages.
- 📊 Integrate visualization of model predictions (e.g., activation maps).
- 🔄 Real-time digit recognition with camera input.
---
## 🌟 Contributors
- Asif Ahmed – [GitHub](https://github.com/asRot0/)
Creator and Maintainer
Want to contribute? Feel free to fork the repository and open a pull request!
## 🤝 Support
If you find this project helpful, consider giving it a ⭐ on [GitHub](https://github.com/asRot0/HandDrawnDigitAI) and sharing it with others!