Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Abandah, Gheith A.; Khedher, Mohammed Z.

doi:10.1142/s1793840609001981

Cited by 15 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extracting features from the whole letter image, as well as, its main body and secondary components provides more valuable features that exploit the recognition potential of the secondary components of handwritten Arabic letters. These results also confirm the importance of the secondary components of the handwritten Arabic letters [8,9] .…”

Section: Discussionsupporting

confidence: 76%

See 1 more Smart Citation

Novel Moment Features Extraction for Recognizing Handwritten Arabic Letters

Abandah¹,

Anssari²

2009

Journal of Computer Science

View full text Add to dashboard Cite

Problem statement: Offline recognition of handwritten Arabic text awaits accurate recognition solutions. Most of the Arabic letters have secondary components that are important in recognizing these letters. However these components have large writing variations. We targeted enhancing the feature extraction stage in recognizing handwritten Arabic text. Approach: In this study, we proposed a novel feature extraction approach of handwritten Arabic letters. Pre-segmented letters were first partitioned into main body and secondary components. Then moment features were extracted from the whole letter as well as from the main body and the secondary components. Using multi-objective genetic algorithm, efficient feature subsets were selected. Finally, various feature subsets were evaluated according to their classification error using an SVM classifier. Results: The proposed approach improved the classification error in all cases studied. For example, the improvements of 20-feature subsets of normalized central moments and Zernike moments were 15 and 10%, respectively. Conclusion/Recommendations: Extracting and selecting statistical features from handwritten Arabic letters, their main bodies and their secondary components provided feature subsets that give higher recognition accuracies compared to the subsets of the whole letters alone.

show abstract

Section: Discussionsupporting

confidence: 76%

“…The Arabic alphabet has 28 basic letters [1,8] . Arabic is written from right to left and is always cursive.…”

Section: Important Features Of Arabic Writingmentioning

confidence: 99%

Novel Moment Features Extraction for Recognizing Handwritten Arabic Letters

Abandah¹,

Anssari²

2009

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…More than half the Arabic letters have main body and secondary components [13]. The secondary components are letter components that are disconnected from the main body such as dots and diacritics.…”

Section: ) Secondary Bodies Identificationmentioning

confidence: 99%

Recognizing handwritten Arabic script through efficient skeleton-based grapheme segmentation algorithm

Abandah

Jamour

2010

2010 10th International Conference on Intelligent Systems Design and Applications

Self Cite

View full text Add to dashboard Cite

To recognize unlimited set of handwritten Arabic words, an efficient segmentation algorithm is needed to segment these cursive words into a limited set of primal graphemes. We propose a rule-based segmentation algorithm that segments cursive words into graphemes through collecting special feature points from the word skeleton. The development of this algorithm is motivated by the need to solve problems and limitations available in the state-of-the-art algorithms in this area. The preliminary evaluation of the proposed algorithm is promising with over 96% accuracy on a sample subset of the IFN/ENIT database.

show abstract

“…One of these reasons is that the vocabulary of the Arabic language is essentially built from the derivation of the roots [32]. The Arabic language has five to seven thousand roots, 85% of these roots are trilateral [31].…”

Section: Introductionmentioning

confidence: 99%

A System for Evaluation of Arabic Root Extraction Methods

Hajjar

Zreik

2010

2010 Fifth International Conference on Internet and Web Applications and Services

View full text Add to dashboard Cite

In this article, we present a new application that evaluated the performance of a number of the Arabic root extraction methods. The implemented methods in this system are selected according to a previous classification, where these methods are classified into five categories. We have selected a method for each category. These methods are: Light Stemmer, Arabic Stemming without a root dictionary, MT-based Arabic Stemmer, N-gram based on similarity coefficient and N-gram based on dissimilarity coefficient. This evaluation was conducted on the same terms in a corpus of two thousand words and their roots. These words are taken from Arabic dictionary "Lesan Al-Arab". This application has allowed us to have a first original comparison between the evaluated methods. This system works in two ways: normal and automatic.

show abstract

Analysis of Handwritten Arabic Letters Using Selected Feature Extraction Techniques

Cited by 15 publications

References 25 publications

Novel Moment Features Extraction for Recognizing Handwritten Arabic Letters

Novel Moment Features Extraction for Recognizing Handwritten Arabic Letters

Recognizing handwritten Arabic script through efficient skeleton-based grapheme segmentation algorithm

A System for Evaluation of Arabic Root Extraction Methods

Contact Info

Product

Resources

About