کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
402991 677036 2011 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition
چکیده انگلیسی

This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation. In the second stage, features are extracted from each of the normalized words, where a set of new features for handwritten Arabic words is proposed, based on a sliding window approach moving across the mirrored word image. The third stage is for classification and recognition, where machine learning is applied using HMMs and DBNs. In order to validate the techniques, extensive experiments were conducted using the IFN/ENIT database which contains 32,492 Arabic words. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 24, Issue 5, July 2011, Pages 680–688
نویسندگان
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