Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/97
Title: Features for HMM-Based Arabic Handwritten Word Recognition Systems
Authors: Likforman-Sulem, Laurence
Hajj Mohamad, Ramy Al
Mokbel, Chafic 
Menasri, Fares
Bernard, Anne-Laure Bianne
Kermorvant, Christopher
Affiliations: Department of Electrical Engineering 
Issue Date: 2012
Part of: V. Märgner & H. El Abed (Eds.), Guide to OCR for Arabic Scripts. Springer.
Start page: 123
End page: 143
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.
URI: https://scholarhub.balamand.edu.lb/handle/uob/97
Type: Book Chapter
Appears in Collections:Department of Electrical Engineering

Show full item record

Record view(s)

1
checked on May 6, 2021

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.