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EvansNjogu / repository
This repository contains a Jupyter Notebook with the implementation of a Sudoku Solver using Deep Learning. The goal is to solve a Sudoku puzzle through image recognition of the puzzle and using a Convolutional Neural Network (CNN) to predict the numbers for each cell.
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This project is a Sudoku Solver that leverages Convolutional Neural Networks (CNNs) to recognize handwritten digits from a Sudoku puzzle image. The model is trained on a dataset of handwritten digits and is used to extract, classify & solve Sudoku puzzles using OpenCV and TensorFlow.
The dataset used for training the model is stored in Kaggle Dataset/Digits, consisting of handwritten digit images (0-9) categorized into subdirectories corresponding to their labels.
The images are loaded, resized to 32x32 pixels, and converted to grayscale. Then, they undergo histogram equalization for contrast enhancement, followed by normalization to scale pixel values between 0 and 1.
A CNN model is used for digit recognition, with two convolutional layers, followed by max pooling, dropout & a fully connected layer.
The model is compiled using RMSprop optimizer and trained for 50 epochs using an ImageDataGenerator for data augmentation.
The input Sudoku puzzle image is processed using OpenCV to detect and extract the Sudoku grid.
Each Sudoku cell is cropped and classified using the trained CNN model. The extracted digits are then used to form a 9x9 Sudoku grid, which is solved using Backtracking Algorithm.
The model successfully extracts and solves Sudoku puzzles with high accuracy. However, misclassifications can occur if digit recognition confidence is below 65%.
For any questions or collaboration opportunities, feel free to reach out at hey@njoguevans.me.
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