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.