Directional Zernike moments for rotation-free recognition of online sketched symbols

A new description method is proposed for sketched symbol recognition. It incorporates local direction information into the Zernike moments which represent only the spatial distribution of sample points. A symbol is decomposed into several component patterns according to the local direction of sample points before Zernike moments computation. The resulting descriptor inherits from the traditional Zernike moments descriptor the invariability to stroke number, stroke order and symbol rotation. Moreover, the fusion of both types of data makes it more informative and discriminative, resulting in better performances in both rotation-invariant classification and rotation angle estimation.