Submit

Login Login

Register Register

Home / Advanced Search
News & Announcements

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
Show export options
  • Articles
  • Online
Search Results (22)

  • Open Access

    ARTICLE

    A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

    Maha Al-Yahya1, *

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 421-433, 2019, DOI:10.32604/cmc.2019.06209

    Abstract The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of More >

    View

    3352

    Download

    2154

    Cited by

    3

  • Open Access

    ARTICLE

    Efficient Analysis of Vertical Projection Histogram to Segment Arabic Handwritten Characters

    Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 55-66, 2019, DOI:10.32604/cmc.2019.06444

    Abstract The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained. More >

    View

    3047

    Download

    1949

    Cited by

    2

Displaying 21-30 on page 3 of 22. Per Page  
123

Contact Us

Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated