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Here’s a more professional and detailed README file for your Handwritten Digit Recognition Web App:
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# Handwritten Digit Recognition Using Convolutional Neural Network (CNN)
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---
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# Handwritten Digit Recognition Web App
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Welcome to the Handwritten Digit Recognition project! This project uses a Convolutional Neural Network (CNN) model trained on the MNIST dataset to recognize handwritten digits (0-9) from user inputs. This application provides a simple web interface where users can draw a digit, and the model will predict the number with high accuracy.
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Welcome to the **Handwritten Digit Recognition Web App** – an interactive application powered by deep learning, specifically designed to recognize handwritten digits in real-time. Leveraging a Convolutional Neural Network (CNN) trained on the MNIST dataset, this app demonstrates the capabilities of modern machine learning in computer vision.
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![App Screenshot](path_to_image_or_gif_showcasing_app) <!-- Optional: Insert a screenshot or gif of the app here -->
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## Table of Contents
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- [Overview](#overview)
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- [Features](#features)
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- [Project Overview](#project-overview)
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- [Demo](#demo)
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- [Technologies Used](#technologies-used)
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- [How It Works](#how-it-works)
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- [Setup and Installation](#setup-and-installation)
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- [Usage Instructions](#usage-instructions)
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- [Features](#features)
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- [Project Structure](#project-structure)
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- [Installation](#installation)
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- [Usage](#usage)
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- [How the Model Works](#how-the-model-works)
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- [Future Applications](#future-applications)
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- [Contributing](#contributing)
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- [License](#license)
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- [Contact](#contact)
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## Overview
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---
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The **Handwritten Digit Recognition Web App** is a simple yet powerful tool that allows users to draw a digit on a canvas and instantly see its prediction. Using a CNN model trained on the MNIST dataset, the app can classify digits from 0 to 9 with high accuracy. This project is ideal for demonstrating the potential of deep learning in handwriting recognition.
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## Project Overview
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## Features
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This project leverages a CNN model to recognize handwritten digits. The model is trained on the MNIST dataset, a well-known dataset for image classification tasks. Users can interact with the application via a simple web interface, where they can draw a digit and get real-time predictions.
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- **Real-Time Drawing Canvas**: Users can freely draw any digit (0-9) on an interactive canvas.
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- **Instant Predictions**: Upon clicking "Predict," the app identifies the digit and displays the result along with a confidence score.
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- **User-Friendly Interface**: A clean, responsive design with step-by-step instructions to guide users.
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- **Accurate Predictions**: Leveraging a trained CNN model for reliable and accurate digit recognition.
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The application uses **HTML, CSS, and JavaScript** for the frontend and **TensorFlow.js** to run the model directly in the browser, ensuring a fast and responsive experience.
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---
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## Demo
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You can try the app by following the setup instructions below or check out a live demo if available.
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Watch a complete walkthrough of the demo of project on YouTube: [YouTube Tutorial](https://www.youtube.com/watch?v=YourVideoIDHere)
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![App Screenshot](path_to_screenshot)
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In this video, you’ll learn:
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- How the model was trained.
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- How to set up the project.
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- How the frontend interacts with the model.
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- And much more!
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## Technologies Used
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---
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- **Python**: Core language for backend and model integration.
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- **TensorFlow/Keras**: For creating and training the CNN model.
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- **Flask**: Lightweight framework to build the backend of the web application.
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- **JavaScript, HTML, CSS**: For creating an interactive and responsive frontend.
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- **NumPy and Pillow**: For handling image preprocessing and manipulation.
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## Features
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## How It Works
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- **Digit Drawing Interface**: Users can draw any digit (0-9) on the canvas.
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- **Real-time Prediction**: The model predicts the drawn digit instantly with high accuracy.
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- **Confidence Score**: Shows the confidence level of the prediction, helping users understand the model's certainty.
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- **Fully Offline**: The model runs in the browser using TensorFlow.js, so no server is needed.
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The web app consists of a simple workflow:
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1. **Draw a Digit**: The user draws a digit (0-9) on the canvas using their mouse or touch input.
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2. **Preprocess Input**: The drawn image is resized and preprocessed to match the input dimensions required by the CNN model.
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3. **Predict Digit**: The preprocessed image is passed to the CNN model, which predicts the digit and returns the result along with a confidence score.
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4. **Display Result**: The predicted digit and confidence score are displayed, giving users immediate feedback.
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---
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### Model Architecture
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The CNN model is trained on the MNIST dataset, which consists of 60,000 images of handwritten digits. It includes layers such as:
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- **Convolutional Layers**: Extract features like edges and textures.
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- **Pooling Layers**: Reduce spatial size and computation, making the model translation-invariant.
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- **Fully Connected Layers**: Analyze high-level features for classification.
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## Project Structure
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```
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Handwritten_Digit_Recognition/
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├── model/
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│ ├── train_model.ipynb # Python script for training the model
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│ ├── convert_model.py # Script to convert the trained model to TensorFlow.js format
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│ └── digit_recognition_model.h5 # Trained model saved in HDF5 format
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├── js/
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│ ├── script.js # JavaScript for UI and model loading
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│ └── model_files/
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│ ├── model.json # TensorFlow.js model architecture
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│ └── group1-shard1of1.bin # Model weights
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├── css/
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│ └── styles.css # Styling for the web interface
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├── samples/
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│ └── example_digit.png # Example image of a digit
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├── .github/workflows/
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│ └── static.yml # GitHub Actions workflow (optional)
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├── Handwritten_Digit_Classification_project.ipynb # Jupyter notebook for training
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├── requirements.txt # Python dependencies for model training
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├── README.md # Project documentation
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├── LICENSE # Project license
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├── index.html # Main HTML file for the web app
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└── CONTRIBUTING.md # Contribution guidelines
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```
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## Setup and Installation
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---
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## Installation
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### Prerequisites
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- Python 3.7 or above
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- Git for cloning the repository
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- Virtual environment setup (recommended)
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- **Python 3.7+** for training the model (if you want to retrain it).
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- **Node.js** and **npm** if you need to use TensorFlow.js locally.
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### Installation Steps
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### 1. Clone the Repository
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1. **Clone the repository**:
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```bash
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git clone https://github.com/yourusername/Handwritten_Digit_Recognition.git
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cd Handwritten_Digit_Recognition
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```
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```bash
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git clone https://github.com/yourusername/handwritten-digit-recognition.git
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cd handwritten-digit-recognition
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```
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### 2. Install Python Dependencies
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2. **Set up a virtual environment** (optional but recommended):
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If you plan to retrain the model, install the necessary Python libraries:
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```bash
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python -m venv env
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source env/bin/activate # For Windows, use `env\Scripts\activate`
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```
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```bash
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pip install -r requirements.txt
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```
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3. **Install required dependencies**:
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### 3. Convert the Model for TensorFlow.js
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```bash
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pip install -r requirements.txt
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```
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After training or modifying the model, you can convert it to TensorFlow.js format:
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4. **Run the Flask app**:
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```bash
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tensorflowjs_converter --input_format keras model/digit_recognition_model.h5 js/model_files
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```
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```bash
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python app.py
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```
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### 4. Open the `index.html` File
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5. **Access the application**:
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Open a web browser and navigate to `http://localhost:5000` to start using the app.
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To run the app, simply open `index.html` in a browser. No server setup is required.
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## Usage Instructions
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---
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1. **Draw a Digit**: On the canvas, use your mouse or finger to draw a digit from 0 to 9.
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2. **Click "Predict"**: Once you are finished, press the "Predict" button.
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3. **View Prediction**: The predicted digit and confidence level will appear on the screen.
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## Usage
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1. **Open `index.html`**: Open the file in any modern browser (Google Chrome, Firefox, etc.).
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2. **Draw a Digit**: Use the canvas to draw a digit from 0 to 9.
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3. **Click 'Predict'**: After drawing, click the 'Predict' button to see the model's prediction.
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4. **View Prediction and Confidence Score**: The prediction and confidence level will display below the canvas.
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---
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## How the Model Works
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1. **Data Preparation**: The model is trained on the MNIST dataset, a set of 60,000 handwritten digit images.
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2. **Model Architecture**: A Convolutional Neural Network (CNN) was used, which is effective for image classification tasks.
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3. **Training and Evaluation**: The model achieves high accuracy on the MNIST test dataset.
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4. **Model Deployment**: The trained model is converted to TensorFlow.js format and loaded in the browser via `script.js`, allowing real-time prediction directly on the client side.
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---
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## Future Applications
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The core technology behind this project – digit recognition with CNNs – can be adapted for numerous real-world applications, including but not limited to:
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This project showcases the potential of digit recognition technology. Here are some potential future applications:
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- **Real-Time Digit Recognition on Mobile**: Expanding the project to mobile devices for on-the-go digit recognition.
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- **Multi-Language Handwriting Recognition**: Extending the model to recognize handwritten text in various languages.
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- **Financial Document Processing**: Automating the digitization of handwritten checks, invoices, and financial records.
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- **Educational Tools**: Developing applications to help young students learn to write and recognize numbers.
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- **Form Processing**: Automating data extraction from handwritten forms in finance, healthcare, and education.
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- **Postal and Logistics Automation**: Identifying ZIP codes and addresses for faster mail sorting and delivery.
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- **Banking Automation**: Digitally reading and processing handwritten checks, deposit slips, and forms.
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- **Educational Tools**: Assisting in automated grading and digital input for tests and assignments.
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- **Mobile App Integration**: Using real-time digit recognition on smartphones for expense tracking, educational apps, and more.
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---
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## Contributing
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Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
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We welcome contributions! If you'd like to improve the project, please:
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1. Fork the repository.
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2. Create a new branch for your feature or bugfix.
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3. Commit your changes and open a pull request with a description of your work.
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## License
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2. Create a new branch: `git checkout -b feature/YourFeature`.
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3. Make your changes.
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4. Submit a pull request.
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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See `CONTRIBUTING.md` for more details.
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## Contact
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---
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For any questions or feedback, please feel free to reach out:
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## License
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- **Email**: your.email@example.com
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- **LinkedIn**: [Your LinkedIn](https://linkedin.com/in/yourprofile)
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- **GitHub**: [Your GitHub](https://github.com/yourusername)
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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---
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## Acknowledgements
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- **MNIST Dataset**: The handwritten digit images used for training.
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- **TensorFlow.js**: For enabling machine learning in the browser.
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- **YouTube Tutorial**: Special thanks to all viewers and contributors for their support!

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