alt Hacker NewsI'm interested to know about the approaches that you tried with the ML, and then decided to not use. In practice, the options are so many. How did you come up with the final approach - and was there a systematic way to decide which options to go for?
I am not on the research team, rather on the production side of things, so my knowledge on that is pretty limited. I think one of the main takeaways from a lot of the research, though, on both the segmentation side and the ink detection side, is that it's a lot less about what models and techniques and such you use, but how good your training data is. Gathering ground truth is hard, and if you don't have a lot of good ground truth, it doesn't matter if your code is perfect, you'll never get results.