Abstract: In this paper, we propose a convolutional neural network model to recognize and classify handwritten and printed digits present in Sudoku puzzle, which is captured using smartphone camera from various magazines, and printed papers. Sudoku puzzle grid is detected using various image processing and filtering techniques such as adaptive threshold. The system described in the paper is thoroughly tested on a set of 100 Sudoku images captured with smartphone cameras under varying conditions. The system shows promising results with 98% accuracy. Our model can handle more complex conditions often present on images that were taken with phone cameras and the complexity of mixed printed and handwritten digits.

Sang C. Suh and Aghalya Dharshni Manmatharaj. “An Efficient Machine Learning Technique to Classify and Recognize Handwritten and Printed Digits of Sudoku Puzzle”. International Journal of Advanced Computer Science and Applications (IJACSA) 10.6 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100682

@article{Suh2019,
title = {An Efficient Machine Learning Technique to Classify and Recognize Handwritten and Printed Digits of Sudoku Puzzle},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100682},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100682},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Sang C. Suh and Aghalya Dharshni Manmatharaj}
}