Combination of HMM-Based Classifiers for the Recognition of Arabic Handwritten Words

Authors:
R. Al-Hajj;C. Mokbel;L. Likforman-Sulem
Affiliations:
University of Balamand, Faculty of Engineering, PoBox 100 Tripoli, LEBANON;University of Balamand, Faculty of Engineering, PoBox 100 Tripoli, LEBANON;GET-Ecole Nationale Supérieure des Télécommunications/TSI, 46 rue Barrault, 75013 Paris, FRANCE
Venue:
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Year:
2007

Citing 0
Cited 4

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

Knowledge-Based Systems
Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

Pattern Recognition Letters
Offline arabic handwritten text recognition: A Survey

ACM Computing Surveys (CSUR)
KHATT: An open Arabic offline handwritten text database

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a two-stage system for the off-line recognition of cursive Arabic handwritten words. The proposed method is analytic without segmentation, and is able to cope with handwriting inclination and with shifted positions of diacritical marks. First, the recognition stage relies on 3 classifiers based on hidden Markov modelling (HMM). The second stage depends on the combination of these classifiers. The feature vectors used for recognition are related to pixel density distribution and to local pixel configurations. These vectors are extracted on word binary images by using a sliding window approach with different angles. We have experimented different combination schemes. The neural network-based combined system yields best performance on the IFN- ENIT benchmark data base of handwritten names of Tunisian villages/towns.