🧩 SudokuVision

SudokuVision is an OCR-powered Sudoku solver that uses cutting-edge machine learning 🤖 and image processing 📸 techniques to extract Sudoku grids from images and solve them efficiently. Whether your Sudoku puzzle is handwritten ✍️ or printed 📰, SudokuVision ensures an accurate solution every time! 🎯

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✨ Overview

SudokuVision integrates Optical Character Recognition (OCR) for grid extraction 🧠 and deep learning algorithms 🔍 to recognize digits within the grid. The application then uses the PySudoku library 🧩 to solve the puzzle, providing a seamless and fast solution ⏱️.

• OCR-Based Sudoku Grid Detection 📸
• Digit Recognition Using Deep Learning 🤖
• PySudoku-Based Solver 🧩
• Works with Printed and Handwritten Sudoku Puzzles ✍️
• Supports PNG, JPG, JPEG Images 🖼️

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✨ Features

• Grid Extraction: Automatically extracts Sudoku grids from images using advanced image processing techniques 🎯.
• Digit Recognition: Identifies digits within the grid using deep learning models 🔢.
• Fast Solver: Solves puzzles using the PySudoku backtracking solver ⏱️.
• Command Line and Web Interface: Provides both command-line and Streamlit-based web interface for ease of use 🖥️.
• Multiple Image Formats: Works with various image formats like PNG, JPG, and JPEG 📸.
• Real-time Visualization: Displays the solved Sudoku puzzle directly in the web app 🌐 and on the command line interface using OpenCV 💡.

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📊 Datasets Used

The following datasets were used for training the deep learning model for digit recognition and grid extraction:

🧑‍🏫 1. MNIST Dataset

The MNIST dataset, containing 60,000 handwritten digit images 🖋️, was used to train the deep learning model for digit recognition. The dataset includes grayscale images 🖤 of size 28x28 pixels and is ideal for training models to recognize handwritten digits.

• Preprocessing:
  • Grayscale normalization to a range of [0, 1] 🌑.
  • Reshaped to 28x28 pixels 🖼️.

🔠 2. Chars74K Dataset

The Chars74K dataset contains images of characters in various fonts 🔠, including digits, used to supplement the training process with diverse digital font variations 🅰️.

• Preprocessing:
  • Resized to 28x28 pixels 🖼️.
  • Grayscale conversion and normalization to a range of [0, 1] 🌑.

This dataset enhances the model's ability to recognize digits in digital fonts, improving accuracy across various types of input 📏.

✍️ 3. TMNIST Dataset

The TMNIST dataset is another handwritten digit dataset used to further train and diversify the digit recognition capabilities 🤖. It contains images in the same format as MNIST and was used to train the model on additional handwritten digits ✍️.

• Preprocessing:
  • Data is scaled to a range of [0, 1] 🌑.
  • Labels are encoded using LabelEncoder 🔣 and converted to categorical values 📊.

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🔍 How It Works

1. Puzzle Extraction 🧩
   The uploaded image is processed using OpenCV 🖼️ for grid extraction. The grid's edges are detected, and the puzzle is segmented into individual cells 🏷️.

2. Digit Recognition 🔢
   Each individual cell in the grid is processed by a deep learning model that recognizes the digits 🧠. The model is trained on the MNIST, Chars74K, and TMNIST datasets 📊.

3. Puzzle Solving 🧩
   Once the digits are identified, they are passed to the PySudoku solver, which uses a backtracking algorithm 🔄 to solve the puzzle 🧩.

4. Result Display 🎥
   The original and solved puzzles are displayed:

   • Web App 🌐: The result is shown directly in the browser 🌍.
   • Command Line 💻: The solved puzzle is displayed directly in the terminal using OpenCV 🖼️. The result is visualized without saving it to a file, using cv2.imshow() to show the solved puzzle 🧩.

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⚙️ Setup and Installation

To set up SudokuVision 🧩 on your local machine 💻, follow the instructions below:

📝 1. Clone the repository

git clone https://github.com/ArchitJ6/SudokuVision.git  
cd SudokuVision  

📦 2. Install Dependencies

Create a virtual environment (recommended) 🌱 and install required packages:

python -m venv venv  
source venv/bin/activate  # On Windows use `venv\Scripts\activate`  
pip install -r requirements.txt  

📥 3. Download the Datasets

Make sure to download the Chars74K, MNIST, and TMNIST datasets. The datasets should be organized in the following directory structure:

/datasets  
    /Chars74K-Digital-English-Font  
        Extract the files and place the folders for digits 0 to 9 here, each containing images of the corresponding digit (labeled accordingly).  
    /tmnist  
        This dataset contains a `data.csv` file with the data for handwritten digits.  

• MNIST: The MNIST dataset will be used directly from Keras.
• Chars74K-Digital-English-Font: Extract the files and organize them into folders for digits 0 to 9, with images of each digit placed inside the corresponding folder, labeled by the digit.
• TMNIST: This dataset includes a data.csv file that contains the data for handwritten digits.

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🧑‍💻 Usage

🌐 1. Streamlit Interface

To run the web interface using Streamlit 🖥️, follow these steps:

1. Run the Streamlit app:

streamlit run app.py  

2. Upload the Image 🖼️:

   • After the app starts, open the URL provided by Streamlit 🌐.
   • Upload an image of the Sudoku puzzle (printed or handwritten) 🧩.
   • Click "Solve Sudoku" 🧠 to process and get the solution 🧩.

3. Output 🎥:
   The original puzzle with solved values will be displayed directly on the web interface 🌐.

💻 2. Command-Line Interface (CLI)

To use the command-line interface 💻:

1. Run the script:

python solve.py --image <path_to_image> --debug -1  

• --image: Path to the Sudoku image 🖼️.
• --debug: Set to 1 for debug mode 🛠️, which visualizes the grid and digit extraction process 🔍.

2. Output 🎥:
   The solved Sudoku puzzle 🧩 will be displayed directly in the new window using OpenCV 🖼️. The window will automatically close when any key is pressed ⏳.

📚 3. Model Training

To train the model for digit recognition 🧠, use the following script:

python train_model.py  

This will load the datasets 📊, preprocess the data 🔄, train the model 🤖, and save the trained model for future use 💾.

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💡 Best Practices for Usage

To get the most accurate results, keep the following tips in mind:

• Ensure good lighting when capturing handwritten Sudoku puzzles ✍️ for optimal digit recognition.
• Use high-resolution images 🖼️ for better grid and digit extraction.
• For handwritten puzzles, maintain legibility of digits for improved accuracy ✍️.

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🔧 Troubleshooting

If you run into issues, check these common solutions:

• Missing Dependencies: Make sure all packages are installed by running pip install -r requirements.txt 📦.
• Image Processing Errors: Ensure that the uploaded image is clear and contains a proper Sudoku grid 📸.
• Solver Not Working: Make sure the digits are clearly detected by checking the debug output with the --debug flag 🛠️.

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🔒 Security and Privacy

Your uploaded images are processed locally and are not stored long-term. We respect your privacy and ensure that no sensitive information is exposed during the image upload and processing process 🔐.

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🤝 How to Contribute

We welcome contributions! 🎉 To contribute to SudokuVision 🧩, follow these steps:

1. Fork the repository 🍴.
2. Create a new branch (git checkout -b feature-name) 🌱.
3. Make your changes ✍️.
4. Commit your changes (git commit -m 'Add feature') 💬.
5. Push to the branch (git push origin feature-name) 🚀.
6. Open a pull request with a description of your changes 📄.

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🙏 Acknowledgments

• PySudoku Library 🧩: For providing an efficient backtracking-based solver.
• MNIST 📚: For the dataset used for training the digit recognition model.
• Chars74K 🔠: For the dataset of digital fonts, enriching the model's ability to recognize various types of digits.
• TMNIST ✍️: For further diversifying the training data and enhancing recognition accuracy.

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📜 License

This project is licensed under the MIT License ⚖️.