Detection Datasets: Forged Characters for Passport and Driving Licence

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>Detection Datasets: Forged Characters for Passport and Driving Licence</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Detection Datasets: Forged Characters for Passport and Driving Licence"> <meta name="description" content="Forged characters detection from personal documents including a passport or a driving licence is an extremely important and challenging task in digital image forensics, as forged information on personal documents can be used for fraud purposes including theft, robbery etc. For any detection task i.e. forged character detection, deep learning models are data hungry and getting the forged characters dataset for personal documents is very difficult due to various reasons, including information privacy, unlabeled data or existing work is evaluated on private datasets with limited access and getting data labelled is another big challenge. To address these issues, we propose a new algorithm that generates two new datasets named forged characters detection on passport (FCD-P) and forged characters detection on driving licence (FCD-D). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm first reads the plain image, then performs forging tasks i.e. randomly changes the position of the random character or randomly adds little noise. At the same time, the algorithm also records the bounding boxes of the forged characters. To meet real world situations, we perform multiple data augmentation on cards very carefully. Overall, each dataset consists of 15000 images, each image with size of 950 x 550. Our algorithm code, FCD-P and FCD-D are publicly available"/> <meta name="keywords" content="Character detection dataset, Deep learning forgery, Forged character detection"/>   <meta name="dc.title" content="Detection Datasets: Forged Characters for Passport and Driving Licence "> <meta name="citation_authors" content="Junjie Li"> <meta name="citation_authors" content="Hui Cao"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT) Vol. 12, No.02"> <meta name="dc.date" content="2022/01/21"> <meta name="dc.identifier" content="https://doi.org/10.5121/csit.2022.120204"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="Forged characters detection from personal documents including a passport or a driving licence is an extremely important and challenging task in digital image forensics, as forged information on personal documents can be used for fraud purposes including theft, robbery etc. For any detection task i.e. forged character detection, deep learning models are data hungry and getting the forged characters dataset for personal documents is very difficult due to various reasons, including information privacy, unlabeled data or existing work is evaluated on private datasets with limited access and getting data labelled is another big challenge. To address these issues, we propose a new algorithm that generates two new datasets named forged characters detection on passport (FCD-P) and forged characters detection on driving licence (FCD-D). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm first reads the plain image, then performs forging tasks i.e. randomly changes the position of the random character or randomly adds little noise. At the same time, the algorithm also records the bounding boxes of the forged characters. To meet real world situations, we perform multiple data augmentation on cards very carefully. Overall, each dataset consists of 15000 images, each image with size of 950 x 550. Our algorithm code, FCD-P and FCD-D are publicly available."> <meta name="dc.subject" content="Character detection dataset"> <meta name="dc.subject" content=" Deep learning forgery"> <meta name="dc.subject" content="Forged character detection">   <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="2022/01/21"> <meta name="prism.volume" content="12"> <meta name="prism.number" content="02"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="41">   <meta name="citation_journal_title" content="Computer Science & Information Technology (CS & IT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Teerath Kumar, Muhammad Turab, Shahnawaz Talpur, Rob Brennan and Malika Bendechache"> <meta name="citation_title" content="Detection Datasets: Forged Characters for Passport and Driving Licence "> <meta name="citation_online_date" content="2022/01/21"> <meta name="citation_issue" content="02"> <meta name="citation_firstpage" content="41"> <meta name="citation_authors" content="Teerath Kumar"> <meta name="citation_authors" content="Muhammad Turab"> <meta name="citation_authors" content="Shahnawaz Talpur"> <meta name="citation_authors" content="Rob Brennan"> <meta name="citation_authors" content="Malika Bendechache"> <meta name="citation_doi" content="https://doi.org/10.5121/csit.2022.120204"> <meta name="citation_abstract_html_url" content="https://aircconline.com/csit/abstract/v12n2/csit120204.html"> <meta name="citation_pdf_url" content="https://aircconline.com/csit/papers/vol12/csit120204.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/papers/vol12/csit120204.pdf"> <meta property="og:title" content="Detection Datasets: Forged Characters for Passport and Driving Licence"> <meta property="og:description" content="Forged characters detection from personal documents including a passport or a driving licence is an extremely important and challenging task in digital image forensics, as forged information on personal documents can be used for fraud purposes including theft, robbery etc. For any detection task i.e. forged character detection, deep learning models are data hungry and getting the forged characters dataset for personal documents is very difficult due to various reasons, including information privacy, unlabeled data or existing work is evaluated on private datasets with limited access and getting data labelled is another big challenge. To address these issues, we propose a new algorithm that generates two new datasets named forged characters detection on passport (FCD-P) and forged characters detection on driving licence (FCD-D). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm first reads the plain image, then performs forging tasks i.e. randomly changes the position of the random character or randomly adds little noise. At the same time, the algorithm also records the bounding boxes of the forged characters. To meet real world situations, we perform multiple data augmentation on cards very carefully. Overall, each dataset consists of 15000 images, each image with size of 950 x 550. 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To address these issues, we propose a new algorithm that generates two new datasets named forged characters detection on passport (FCD-P) and forged characters detection on driving licence (FCD-D). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm first reads the plain image, then performs forging tasks i.e. randomly changes the position of the random character or randomly adds little noise. At the same time, the algorithm also records the bounding boxes of the forged characters. To meet real world situations, we perform multiple data augmentation on cards very carefully. Overall, each dataset consists of 15000 images, each image with size of 950 x 550. Our algorithm code, FCD-P and FCD-D are publicly available. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Character detection dataset, Deep learning forgery, Forged character detection.</p><br> <button type="button" id="button"><a target="_blank" href="/csit/papers/vol12/csit120204.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/csit/V12N02.html">Volume 12, Number 2</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li id="id"><a href="http://airccse.org/editorial.html">Editorial Board</a></li> <li><a href="http://airccse.org/arch.html">Archives</a></li> <li><a href="http://airccse.org/indexing.html">Indexing</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div> <div class="clear_left"></div> <br> </div> <div class="clear"></div> <div id="footer"> <table width="100%" > <tr> <td width="46%" class="F_menu"><a href="http://airccse.org/subscription.html">Subscription</a> <a href="http://airccse.org/membership.html">Membership</a> <a href="http://airccse.org/cscp.html">AIRCC CSCP</a> <a href="http://airccse.org/acontact.html">Contact Us</a> </td> <td width="54%" align="right"><a href="http://airccse.org/index.php"><img src="/csit/abstract/img/logo.gif" alt="" width="21" height="24" /></a><a href="http://www.facebook.com/AIRCCSE"><img src="/csit/abstract/img/facebook.jpeg" alt="" width="21" height="24" /></a><a href="https://twitter.com/AIRCCFP"><img src="/csit/abstract/img/twitter.jpeg" alt="" width="21" height="24" /></a><a href="http://cfptech.wordpress.com/"><img src="/csit/abstract/img/index1.jpeg" alt="" width="21" height="24" /></a></td> </tr> <tr><td height="25" colspan="2"> <p align="center">All Rights Reserved ® AIRCC</p> </td></tr> </table> </div> </div> </div> </div> </div> </div> </body> </html>

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Detection Datasets: Forged Characters for Passport and Driving Licence