Abdullah A. AlShaher, Public Authority for Applied Education and Training, Kuwait

This paper demonstrates how 2D handwritten shapes can be classified by analyzing shape structure. The underlying framework is a one-layer architecture where the shapes are segmented to a series of connected segments. Each segment is represented by a set of uniformly distributed landmarks along the skeleton of the character. We follow by representing each segment using the Point Distribution Model (SPDM). We then capture shape variations by learning Gaussian mixture of segment point distribution models in a two-step Expectation Maximization algorithm. The approach is tested on a set of handwritten Arabic characters.

Handwritten Arabic characters, Shape analysis, Point distribution models, Machine learning, Expectation Maximization Algorithm.