A toolbox for real orthogonal polynomials

This paper presents an open-source, cross-platform toolbox for discrete orthogonal polynomials (DOPs), enabling their practical use in scientific computing and signal/image processing workflows. The proposed toolbox includes six DOP families: Hahn, Meixner, Charlier, Krawtchouk, Tchebichef, and Racah polynomials, implemented in C + + , Python, and MATLAB using consistent interfaces across platforms. The toolbox provides routines for constructing orthogonal polynomial bases and using them for forward and inverse polynomial-domain transforms of 1D, 2D, and 3D signals. Since the attainable polynomial order is influenced by numerical conditioning and finite-precision arithmetic, the toolbox is designed to provide reliable performance for practical orders relevant to moment-based and transform applications. Overall, the toolbox facilitates reproducible experimentation and supports both researchers and new users working with DOP-based transforms and moments.