Nonogram is one of the many names for a graphical logic puzzle that originated in 1987 in Japan. Other common names include Griddlers, Paint By Numbers, Hanjie, and Picross. Talking about the gameplay, a nonogram consists of a complex grid with several rows and columns and numerical clues along the top and left sides. Its main objective is to reconstruct the hidden image by figuring out the cells that are to be colored or left blank. The appeal of nonograms is that these are easy, challenging, and entertaining.
Earlier, nonograms were produced by hand, but as technology evolved, puzzle makers started automating the process. And as the production mechanism computerized, so did the solving algorithms. Today, there is a large amount of software available online that solve nonograms without any complexities. This especially comes in handy when you encounter harder instances like nonograms with multiple solutions or massive grids.
Top Best Software
Some of the top software solver for nonograms include, but aren’t limited to the following:
- Griddlers solver with animator
- Pynogram solver and animator (in Python)
- Teal nonogram puzzle and solver
- Nonogrid solver (in Rust)
- Nonogram solver (in CGI)
- Nonogram solver (in C++)
- Nonogram-solver (in Ruby)
- Nonogram-solver (in Python)
- HTML5 Nonogram Solver (in browser)
- QR code generator and solver (in Mathematica)
These computer coded programs employ approaches that are slightly different than the usual row and column technique. They have unique algorithms that scan the grid cell by cell, store the information on the possibilities, and apply the extracted data to generate the solution.
Some nonogram puzzles are tough to solve, aiming to explore unconventional solving techniques. But these types are too rare, and that’s when software solvers come to rescue. Also, these programs help the designers to validate their puzzles and ensure those have unique solutions.
Most recently developed solvers are quicker and more efficient for both simple as well as complicated griddlers. But as these enable computers to solve puzzles, humans can hardly apply any of the associated strategies in real-time.
We picked this topic because we enjoy solving griddler puzzles on the phone, in free time, or even otherwise. We’ve certainly thought about what kind of algorithm humans work on while solving these logic puzzles and how fast they’re at it!
Clearly, it requires much more competence to be as fast and efficient as a computerized solver to get into a competition. Let us know your views on any other solvers you might have used. There are communities online that discuss each of these solvers in detail, assess their algorithms and limitations, and also compare the common ones. Sure, programmers have come really far, but certainly, there’s room for improvement.
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