Features for HMM-based arabic handwritten word recognition systems

Abstract

HMM-based systems need observation sequences as input. These observations consist of discrete values or vectors extracted from word images or text lines. In this chapter we explore various types of features which are popular for Arabic cursive handwriting recognition. Some of these features are statistical, based on pixel distributions or local directions. Others are structural, based on the presence of loops, ascenders, or descenders. We show how these features can be efficient within HMM-based systems based on sliding windows or grapheme segmentation.