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  <doi_batch_id>aspg-3-3657-1791419456</doi_batch_id>
  <timestamp>20261008003056</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <journal>
   <journal_metadata language="en">
    <full_title>Fusion: Practice and Applications</full_title>
    <abbrev_title>FPA</abbrev_title>
    <issn media_type="print">2770-0070</issn>
    <issn media_type="electronic">2692-4048</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>19</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Fast Numeric Sign Detection Using Adaptive Thresholding and Geometry of Optimized Fingers</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mela G.</given_name>
      <surname>Abdul-Haleem</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Loay E.</given_name>
      <surname>George</surname>
      <affiliations>
       <institution>
        <institution_name>College of Science, University of Baghdad, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>A strong sign language recognition system can break down the barriers that separate hearing and speaking members of society from speechless members. A novel fast recognition system with low computational cost for digital American Sign Language (ASL) is introduced in this research. Different image processing techniques are used to optimize and extract the shape of the hand fingers in each sign. The feature extraction stage includes a determination of the optimal threshold based on statistical bases and then recognizing the gap area in the zero sign and calculating the heights of each finger in the other digits. The classification stage depends on the gap area in the zero signs and the number of opened fingers in the other signs as well as the sequence in which the opened fingers appear for those that have the same number of opened fingers. The conducted test results showed the system’s high capability to classify all the digits; where both the precision and F-score percentages of the proposed model reached the desired optimal value (100%).</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>01</first_page>
     <last_page>14</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3657</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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    <doi_data>
     <doi>10.54216/FPA.190201</doi>
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