vincenthwt

The claim that "a tiny NN can learn edges better" is misleading. Classical algorithms like Canny or Sobel are specifically designed for edge detection, making them faster, more reliable, and easier to use in controlled environments. Neural networks need more data, training, and computational power, often just to achieve similar results. For simple edge detection tasks, classical methods are typically more practical and efficient.

If you're looking for simpler, faster, and reliable edge detection algorithms, traditional methods like Canny, Sobel, Prewitt, and Laplacian are excellent choices.

For real-time applications or resource-constrained systems, simpler methods like Roberts or Prewitt may be the best.

However, if you need more robustness against noise or better edge accuracy, Canny or Scharr are preferred.

Yes, those methods are old, but they’re explainable and much easier to debug or improve compared to the black-box nature of neural networks. They’re still useful in many cases.

Since Sudoku is a logic-based game, why not use Prolog to create a solver? Example of Prolog code can be found here, https://www.metalevel.at/sudoku/.