Title :
Invariant Primitives for Handwritten Arabic Script: A Contrastive Study of Four Feature Sets
Author :
Haboubi, Sofiene ; Maddouri, Samia ; Ellouze, Noureddine ; El-Abed, Haikal
Author_Institution :
Image Process. & Pattern Recognition Unit, Nat. Eng. Sch. of Tunis (ENIT), Tunis, Tunisia
Abstract :
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.
Keywords :
feature extraction; handwritten character recognition; image classification; multilayer perceptrons; natural language processing; IFN-ENIT database; feature set; handwriting recognition; handwritten Arabic script; image classification; image database; neural network multilayer perceptron; Data mining; Handwriting recognition; Image analysis; Image databases; Image recognition; Image segmentation; Pattern recognition; Skeleton; Spatial databases; Text analysis; Arabic script; IFN/ENIT; Invariant Primitives;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.281
