Paper Info
Title
Off-line recognition handwriting Arabic words using combination of multiple classifiers
Abstract
We present in this paper a system of Arabic handwriting recognition based on combining methods of decision fusion approach. The proposed approach introduces a methodology using the HMM-Toolkit (HTK) for a rapid implementation of our designed recognition system. After the image preprocessing, the text is segmented into lines, the obtained images are then used for features extraction with Sliding window technique. These features are extracted on binary images of characters and are modeled separately using Hidden Markov Models classifiers. The combination of the multiple HMMs classifiers was applied by using the different methods of decision fusion approach. The proposed system is evaluated using the IFN/ENIT database. Experimental results for Arabic handwritten recognition demonstrate that the Weighted Majority Voting (WMV) combination method have given better recognition rate 76.54% in top1, with Gaussian distribution.
Year
DOI
Venue
2014
10.1109/CIST.2014.7016629
Information Science and Technology
Keywords
DocType
ISSN
Gaussian distribution,feature extraction,handwriting recognition,handwritten character recognition,hidden Markov models,image segmentation,natural language processing,Arabic handwriting offline recognition,Gaussian distribution,HMM-toolkit,HTK,IFN/ENIT database,decision fusion approach,feature extraction,hidden Markov model classifier,multiple HMM classifiers,offline Arabic handwritten recognition,sliding window technique,text segmentation,weighted majority voting combination method,HMM Toolkit (HTK),HMMs,Horizontal Projection,Hough Transform,Sliding Window technique,VH2D,decision fusion approach
Conference
2327-185X
Citations 
PageRank 
References 
0
0.34
10
Authors
4
Name
Order
Citations
PageRank
Maqqor, A.130.79
Akram Halli213.06
Khalid Satori34216.75
Hamid Tairi45717.49