@article{(Open Science Index):https://publications.waset.org/pdf/15261,
	  title     = {Unconstrained Arabic Online Handwritten Words Segmentation using New HMM State Design},
	  author    = {Randa Ibrahim Elanwar and  Mohsen Rashwan and  Samia Mashali},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentation-recognition using HMMs of unique design trained using online features most of which are novel. The HMM output characters boundaries represent the proposed segmentation points (PSP) which are then validated by rules-based post stage without any contextual information help to solve different segmentation errors. The HMM has been designed and tested using a self collected dataset (OHASD) [1]. Most errors cases are cured and remarkable segmentation enhancement is achieved. Very promising word and character segmentation rates are obtained regarding the unconstrained Arabic handwriting difficulty and not using context help.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {4},
	  year      = {2012},
	  pages     = {519 - 527},
	  ee        = {https://publications.waset.org/pdf/15261},
	  url   	= {https://publications.waset.org/vol/64},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 64, 2012},
	}