For the last few months I’ve been focused on building RiichiCam, a free web app (works on any device) that makes scoring hands much easier and faster than using a scoring sheet, especially for players that are newer to playing over the board. You take a photo of your winning hand and the app gives you a full yaku, fu, and payments breakdown.
My first attempt at the vision model only used open-source tile images, plus a couple hundred of my own to fill in the gaps. Its accuracy was spotty at best, and it could really only support one tileset. After deciding to add more variety by rendering thousands of synthetic images for model training, accuracy improved to practically perfect in real-world conditions under a variety of tileset styles. I’ve had a few local clubs test it out and have heard great things.
Moving model inference to on-device instead of routing through a server also cut the time from taking a picture to being able to score the hand down to about 200 milliseconds, which I’m super stoked about.
Would love to hear any suggestions you guys might have to make the app more convenient to use! The scanning feature is the most complex part of the app so I want to make sure it’s easy to understand and fast.
If you’re interested, you can check it out at riichicam.com!
(Disclaimer: the majority of the implementation work on this project was done with generative AI, but the product design and engineering decisions are mine.)