A Survey of Offline Handwriting Signature Verification
Each individual possesses a unique signature that is primarily employed to verify personal identity and authenticate legally binding documents or facilitate significant transactions, a method commonly utilized for verifying their identity. The utilization of this technology is restricted to the aut...
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| Format: | Article |
| Language: | English |
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Al-Kitab University
2025-01-01
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| Series: | Al-Kitab Journal for Pure Sciences |
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| Online Access: | https://isnra.net/index.php/kjps/article/view/1202 |
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| _version_ | 1841536294540279808 |
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| author | Jihad Majeed Nori Asim M. Murshid |
| author_facet | Jihad Majeed Nori Asim M. Murshid |
| author_sort | Jihad Majeed Nori |
| collection | DOAJ |
| description |
Each individual possesses a unique signature that is primarily employed to verify personal identity and authenticate legally binding documents or facilitate significant transactions, a method commonly utilized for verifying their identity. The utilization of this technology is restricted to the authentication of biometric recognition in a range of financial, legal, banking, insurance, and various other business documents. Techniques for recognizing signatures are employed to determine the specific user associated with a particular signature. In recent years, a significant number of researchers have focused on the implementation of novel approaches in this area, with a notable increase in the prevalence of deep learning techniques. To enhance the understanding of the evolution of offline handwritten signature recognition among researchers, this manuscript adopts a structured methodology to categorize this research, drawing primarily from studies found in set major databases. This study assesses methodologies for offline handwritten signature recognition by implementing predetermined inclusion and exclusion criteria. It explores various aspects, such as feature extraction and challenges in classification. In recent years, there have been noticeable advances and new developments. The paper accentuates the dominance of deep learning research directions in this specific domain. Differing from existing surveys, this paper does not confine itself to a particular research phase but meticulously outlines each stage, aspiring to guide future researchers in their investigations.
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| format | Article |
| id | doaj-art-eccf199204c54de9a63bdd6cbd72a296 |
| institution | Kabale University |
| issn | 2617-1260 2617-8141 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Al-Kitab University |
| record_format | Article |
| series | Al-Kitab Journal for Pure Sciences |
| spelling | doaj-art-eccf199204c54de9a63bdd6cbd72a2962025-01-14T19:03:24ZengAl-Kitab UniversityAl-Kitab Journal for Pure Sciences2617-12602617-81412025-01-0190110.32441/kjps.09.01.p8A Survey of Offline Handwriting Signature VerificationJihad Majeed Nori0Asim M. Murshid 1College of Computer Science and Information Technology, University of Kirkuk, Kirkuk, IraqCollege of Computer Science and Information Technology, University of Kirkuk, Kirkuk, Iraq Each individual possesses a unique signature that is primarily employed to verify personal identity and authenticate legally binding documents or facilitate significant transactions, a method commonly utilized for verifying their identity. The utilization of this technology is restricted to the authentication of biometric recognition in a range of financial, legal, banking, insurance, and various other business documents. Techniques for recognizing signatures are employed to determine the specific user associated with a particular signature. In recent years, a significant number of researchers have focused on the implementation of novel approaches in this area, with a notable increase in the prevalence of deep learning techniques. To enhance the understanding of the evolution of offline handwritten signature recognition among researchers, this manuscript adopts a structured methodology to categorize this research, drawing primarily from studies found in set major databases. This study assesses methodologies for offline handwritten signature recognition by implementing predetermined inclusion and exclusion criteria. It explores various aspects, such as feature extraction and challenges in classification. In recent years, there have been noticeable advances and new developments. The paper accentuates the dominance of deep learning research directions in this specific domain. Differing from existing surveys, this paper does not confine itself to a particular research phase but meticulously outlines each stage, aspiring to guide future researchers in their investigations. https://isnra.net/index.php/kjps/article/view/1202Offline Handwritten SignatureTraditional MethodsMachine LearningDeep Learning |
| spellingShingle | Jihad Majeed Nori Asim M. Murshid A Survey of Offline Handwriting Signature Verification Al-Kitab Journal for Pure Sciences Offline Handwritten Signature Traditional Methods Machine Learning Deep Learning |
| title | A Survey of Offline Handwriting Signature Verification |
| title_full | A Survey of Offline Handwriting Signature Verification |
| title_fullStr | A Survey of Offline Handwriting Signature Verification |
| title_full_unstemmed | A Survey of Offline Handwriting Signature Verification |
| title_short | A Survey of Offline Handwriting Signature Verification |
| title_sort | survey of offline handwriting signature verification |
| topic | Offline Handwritten Signature Traditional Methods Machine Learning Deep Learning |
| url | https://isnra.net/index.php/kjps/article/view/1202 |
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