Well-Conditioned Polynomial Representations for Mathematical Handwriting Recognition
This work addresses computational efficiency in mathematical handwriting recognition, but it appears incremental as it builds on prior parameterized curve representations.
The paper investigates the trade-offs between basis choice and polynomial degree for mathematical handwriting recognition, focusing on condition numbers and norm bounds to achieve accurate modeling with low computational cost.
Previous work has made use of a 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. This article explores 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.