IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v14y2020i4p82-100.html
   My bibliography  Save this article

A Multi-Agent Approach to Segment Arabic Handwritten Text Lines

Author

Listed:
  • Mohsine Elkhayati

    (University Ibn Tofail, Morocco)

  • Youssfi Elkettani

    (University Ibn Tofail, Morocco)

  • Mohammed Mourchid

    (University Ibn Tofail, Morocco)

Abstract

In text line segmentation, there are three classes of methods: either by sorting physical units such as pixels or connected components (CC) constituting a line or by searching for the baseline of each word and grouping together those who participate in the same line. The third class analyzes the separation locations between the lines. After an overview of lines segmentation approaches, the authors introduced a new method emphasizing its simplicity, speed, and originality. The proposed approach detects the starting components of the lines in the first step. In the second step, it defines a number of agents that start the segmentation process from their starting points between the starting components of lines. Each agent aims to reach the left edge of the document through the correct path. The algorithm used by the agents is based on the morphological process, characteristics of the Arabic manuscript and a communication system. The experimental results on an Arabic dataset show that this approach is an effective solution for the segmentation of lines from different Arabic manuscripts.

Suggested Citation

  • Mohsine Elkhayati & Youssfi Elkettani & Mohammed Mourchid, 2020. "A Multi-Agent Approach to Segment Arabic Handwritten Text Lines," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 14(4), pages 82-100, October.
  • Handle: RePEc:igg:jcini0:v:14:y:2020:i:4:p:82-100
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2020100105
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jcini0:v:14:y:2020:i:4:p:82-100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.