Using HMM Toolkit (HTK) for Recognition of Arabic Manuscripts Characters

被引：0
|
作者：
Maqqor, Ahlam [1 ]
Halli, Akram [1 ]
Satori, Khalid [1 ]
Tairi, Hamid [1 ]
机构：
[1] Fac Sci Dhar EL Mahraz, Lab LIIAN, Fes, Morocco
来源：
2014 INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS (ICMCS) | 2014年
关键词：
Cursive Arabic; Hidden Markov Models; Horizontal Projection; Sliding Window; Features of Local Densities; Features Statistical; Training Phases; Testing Phases; HMM Toolkit (HTK);
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose an analytical approach of an offline recognition of handwritten Arabic. Our method is based on Hidden Markov Models (HMM) Toolkit (HTK), modeling type that takes into consideration the characteristics of Arabic script and possible inclinations of cursive words. The objective is to propose a methodology for rapid implementation of our approach. To this end, a preprocessing phase that can prepare the data was introduced. These data are then used by an extraction method of two groups of the characteristics (Features of Local Densities and Features Statistical) with the use of the technique of sliding windows, the results of this step are processed in sequence information as vectors to HTK (Hidden Markov Model Toolkit).
引用
收藏
页码：475 / 479
页数：5
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