Speaker: Sunsern Cheamanunkul

Title: Co-adaptation for handwriting recognition


Human computer interaction tasks, such as handwriting recognition, pose an interesting and challenging machine learning problem. There is no single correct way to write a character. As a result, in addition to the possibility of having the machine adapt to the handwriting of the user, there is a viable possibility of having the user adapt their handwriting so that it is easier to recognize.

In this talk, I will present a framework for handwriting recognition that considers the writing trajectory as a continuous-time communication channel with feedback. As the user is writing, the recognition algorithm constantly re-evaluates the likelihoods of different letters. The recognition system provides the user with real-time feedback to help the user identify mistakes early. In addition, offline analysis is used to guide the user how to make adjustments to their writing that will improve the recognition accuracy.