Journal Press India^®

Author Details ( * ) denotes Corresponding author

In an era increasingly shaped by technology, the enduring allure of word puzzles, such as word searches, crosswords, and Sudoku, remains steadfast. Yet, manually tackling these puzzles can prove to be a time-intensive and frequently exasperating endeavor. To meet this challenge, we suggest employing image recognition-based algorithms designed for puzzle-solving, specifically targeting Sudoku, word searches, and crossword puzzles. EnigmaScan is publicly accessible at https://github.com/theNewtonCode/EnigmaScan, allowing users to promptly solve their puzzles in real-time by uploading snapshots of unsolved Sudoku, word searches, or crosswords. This ground-breaking tool is designed to streamline and hasten the puzzle-solving process for users of all proficiency levels, thereby promoting a more enjoyable and accessible interaction with these timeless pastimes. The proposed algorithms exhibit remarkable efficiency, solving these puzzles within seconds and achieving an accuracy of 100%.

Keywords

Sudoku; Word Search; Crossword; Puzzle Solving; Dancing Links Algorithm; Optical Character Recognition

1. Berg, B., Høgskar, D., & Torbjørnsen, Ø. (2003). A fast parallel algorithm for solving sudoku puzzles. In Proceedings of the Fourth International Symposium on Artificial Intelligence and Mathematics.
2. Bradski, G., & Kaehler, A. (2000). Opencv: A general-purpose computer vision library. Dr. Dobb’s Journal of Software Tools, 25(11), 120-123.
3. Chen, H., Tsai, S.S., Schroth, G., Chen, D.M., Grzeszczuk, R., & Girod, B. (2011). Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In 2011 18^th IEEE International Conference on Image Processing, 2609-2612. IEEE.
4. Colton, S., Bacardit, J., & Ross, P. (2007). An efficient and flexible algorithm for solving crossword puzzles. AI Communications, 20(3), 181-189.
5. Crawford, B., Castro, C., & Monfroy, E. (2009). Solving sudoku with constraint programming. In 20^th International Conference, MCDM 2009, Chengdu/ Jiuzhaigou, China, June 21-26, 2009. Proceedings (pp. 345-348). Springer.
6. Damas, F.B.V., & Oliveira, M.H.M.B. (2004). A survey of constraint programming techniques for the sudoku puzzle. In European Conference on Artificial Intelligence (pp. 735-739).
7. Haythorpe, M. (2016). Reducing the generalised sudoku problem to the hamiltonian cycle problem. AKCE International Journal of Graphs and Combinatorics, 13(3), 272-282.
8. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385.
9. Hutchings, R.A., & Pothen, A. (2019). Solving large sudoku puzzles faster. arXiv preprint arXiv:1905.12965.
10. Jaderberg, M., Simonyan, K., & Zisserman, A. (2014). Reading scene text in deep convolutional sequences. In Proceedings of the International Conference on Learning Representations (ICLR).
11. Knuth, D.E. (2000). Dancing links. arXiv:cs.DS/0011047 v1.
12. Kusakari, K., & Hashimoto, J. (2010). Solving and rating large-scale sudoku problems via partitioning. In International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming (pp. 297-310).
13. Littman, M.L., Keim, G.A., & Shazeer, N. (2002). A probabilistic approach to solving crossword puzzles. Artificial Intelligence, 134(1-2), 23-55.
14. Jam Soft LLC (n.d.): Asolver>let’s solve the puzzle. Retrieved from https://apps.apple.com/us/app/asolver-lets-solve-the-puzzle/id1505663005
15. Mishra, A., Alahari, K., & Jawahar, C.V. (2013). Robust text detection in natural images with edge-enhanced maximally stable extremal regions. arXiv preprint arXiv:1312.4894.
16. Nguyen, A.D., Do, T.T., Nguyen, T., & Cheung, N.M. (2017). Sudokuvision: Solving and explaining sudoku puzzles using vision. arXiv preprint arXiv:1704.03004.
17. Prosser, P. (1998). Solving crossword puzzles as a constraint satisfaction problem. In Proceedings of the European Conference on Artificial Intelligence (ECAI), (vol. 98, pp. 429-433).
18. Rosebrock, A. (2020): Opencv sudoku solver and ocr. Retrieved from https://pyimagesearch.com/2020/08/10/opencv-sudoku-solver-and-ocr
19. Shazeer, N.M., Littman, M.L., & Keim, G.A. (1999). Solving crossword puzzles as probabilistic constraint satisfaction. AAAI Press, Palo Alto: California USA.
20. Abto Software (2019). Computer vision powers automatic jigsaw puzzle solver. Retrieved from https://www.abtosoftware.com/blog/computer-vision-powers-automatic-jigsaw-puzzle-solver
21. Vezina, M. (2017). On project estimation or how long does it take to solve a sudoku. Retrieved from https://medium.com/@m_vezina/on-project-estimation-or-how-long-does-it-take-to-solve-a-sudoku-881dc41d341e