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15 results on '"Khayyat, Muna"'

2. Novel Feature Extraction Methods to Detect Age from Handwriting

Author

AL-Qawasmeh, Najla, Khayyat, Muna, Suen, Ching Y., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Carmona-Duarte, Cristina, editor, Diaz, Moises, editor, Ferrer, Miguel A., editor, and Morales, Aythami, editor

Published
2022
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4. Automating Stress Detection from Handwritten Documents

Author

AL-Qawasmeh, Najla, Khayyat, Muna, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lu, Yue, editor, Vincent, Nicole, editor, Yuen, Pong Chi, editor, Zheng, Wei-Shi, editor, Cheriet, Farida, editor, and Suen, Ching Y., editor

Published
2020
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11. Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits.

Author

El Hindi, Khalil, Khayyat, Muna, and Abu Kar, Areej

Subjects
ABILITY testing, FOURIER transforms, HISTOGRAMS, ARABIC alphabet, HINDI language, NUMBER concept
Abstract

The main aim of this work is to compare Hindu and Arabic digits with respect to a machine’s ability to recognize them. This comparison is done on the raw representation (images) of the digits and on their features extracted using two feature selection methods. Three learning algorithms with different inductive biases were used in the comparison performed using the raw representation; two of them were also used to compare the digits using their extracted features. All classifiers gave better results for Hindu digits in both cases; when raw representation was used and when the selected features where used. The experiments also show that Hindu digits can be classified with better accuracy, higher confidence and using fewer features than Arabic digits. These results indicate that hand-written Hindu digits are actually easier to recognize than hand-written Arabic digits. The machine learning methods used in this work are instance based learning (the kNN algorithm), Naïve Bayesian and neural networks. The feature extraction methods we used were Fourier transformation and histograms. [ABSTRACT FROM AUTHOR]

Published
2017
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15. Arabic Handwritten Word Spotting Using Language Models.

Author

Khayyat, Muna, Lam, Louisa, and Suen, Ching Y.

Abstract

With the ever-increasing amounts of published materials being made available, developing efficient means of locating target items has become a subject of significant interest. Among the approaches adopted for this purpose is word spotting, which enables the identification of documents through the use of pertinent keywords. This paper reports on an effective method of word spotting for Arabic handwritten documents that takes into consideration the nature of Arabic handwriting. Parts of Arabic Words (PAWs) form the basic components of this search process, and a hierarchical classifier (consisting of a set of classifiers each trained on a different part of the input pattern) is implemented. For the first time in Arabic word spotting, language models are incorporated into the process of reconstructing words from PAWs. Details of the method and promising experimental results are also presented. [ABSTRACT FROM PUBLISHER]

Published
2012
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