Please note: This PhD seminar will take place in DC 3317 and online.
Deepak Singh Kalhan, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Stephen M. Watt, Robert M. Corless
Considering digital ink as plane curves provides a valuable framework for various applications, including signature verification, note-taking, and mathematical handwriting recognition. These plane curves can be obtained as parameterized pairs of approximating truncated series $\big ( x(s), y(s) \big )$ determined by sampled points. Previous work has made use of these parameterized plane curve polynomial representation for mathematical handwriting, with the polynomials represented in a Legendre or Legendre-Sobolev graded basis. This provides a compact geometric representation for the digital ink. Preliminary results have also been shown for Chebyshev and Chebyshev–Sobolev bases.
In this work, we explore the trade-offs between basis choice and polynomial degree to achieve accurate modeling with a low computational cost. To do this, we consider the condition number for polynomial evaluation in these bases and bound how the various inner products give norms for the variations between symbols. We have also run experiments on the UNIPEN dataset using Legendre, Chebyshev, Legendre-Sobolev, and Chebyshev-Sobolev polynomial bases to evaluate their effectiveness in representing handwritten symbols.
Join by:
- In-person: DC 3317
- Online: MS Teams