Optimized real-time sudoku puzzle solving and solution overlay on images and videos using advanced computer vision and TensorFlow

Abstract

This paper presents a novel approach to solving Sudoku puzzles using computer vision techniques. Sudoku, a popular logic-based number placement game, poses an interesting challenge for automated solutions. The proposed Sudoku solver employs image processing and computer vision algorithms to extract the puzzle grid from a given image. The system then employs optical character recognition (OCR) to recognize and digitize the numbers within the grid. The image preprocessing involves techniques such as edge detection, contour identification, and perspective transformation to enhance the accuracy of grid extraction. Subsequently, OCR is applied to recognize the digits within each cell of the Sudoku grid. The solver then utilizes a backtracking algorithm to systematically fill in the missing numbers while adhering to the rules of Sudoku. The computer vision-based Sudoku solver offers advantages in terms of versatility, as it can handle puzzles of varying difficulty levels and appearances. The system's performance is evaluated on a diverse set of Sudoku puzzles, demonstrating its effectiveness in accurately solving puzzles with different grid sizes and layouts. The results indicate that the proposed approach achieves competitive solving times while maintaining a high level of accuracy. The presented Sudoku solver serves as a testament to the potential of computer vision in solving complex puzzles, opening avenues for further research in the integration of artificial intelligence and image processing for recreational problem-solving applications.