Invariant Primitives for Handwritten Arabic Script: A Contrastive Study of Four Feature Sets

The choice of relevant features is very decisive in handwriting recognition rate. Our aim is to present some useful structural and statistical features and see their degree of variability. In this paper, we start with a description of the variability of the Arabic handwriting and the way how to reduce it. Four kinds of feature sets used by our handwriting systems are then presented evaluated and discussed. The comparison is carried on a database of images from IFN/ENIT databases. The Neural Network Multilayer perceptrons is our method of classification. A contrastive study of these primitives is done according to recognition their time and memory consuming and their variability degree.

• Type: Journal
• Year: 2009
• Where: ICDAR
• Authors: Sofiene Haboubi, Samia Maddouri, Noureddine Ellouze, Hailkal El Abed
• Keywords: Document Analysis, Handwriting Recognition, ICDAR 2009, Neural Network Multilayer, Variability Degree