Optimized detection of Urdu signatures in real-world images using YOLO v7
Authentication plays an important role in managing security. Signature is one of the first broadly practiced method to authenticate an individual. However, existing research is solely based on the English signature detection and recognition with limited work on low-resource languages. Although being...
| Authors: | , |
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| Format: | article |
| Publication Date: | 2026 |
| Country: | España |
| Institution: | Universitat Autònoma de Barcelona |
| Repository: | Dipòsit Digital de Documents de la UAB |
| Language: | English |
| OAI Identifier: | oai:dnet:uabarcelona_::3dc08dba1d56889e19cebe074fc96532 |
| Online Access: | https://ddd.uab.cat/record/327507 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.2267 |
| Access Level: | Open access |
| Keyword: | Computer vision Image analysis Signature detection Urdusig Deep learning Handwritten signature Yolov7 |
| Summary: | Authentication plays an important role in managing security. Signature is one of the first broadly practiced method to authenticate an individual. However, existing research is solely based on the English signature detection and recognition with limited work on low-resource languages. Although being desirable for the document forensics and security purposes, it remains a challenging task to detect Urdu signatures in realistic settings due to different styles of the Urdu signatures and presence of noise, background, and other nuisance factors. Moreover, the lack of annotated datasets hindered the signature detection in natural environments. To address these challenges, this paper proposes an Urdu signature dataset consisting of more than 5000 official and real-world scanned documents of different genres, i.e., publicly available official government letters, feedback given by the public in relation to various departments, and civil court orders of the Sahiwal region. Furthermore, we proposed the YOLOv7 model for Urdu signature detection. The findings show that the proposed YOLO v7 is effective and accurate in detecting Urdu signatures even under different lighting conditions, background complexities, and signature distortions. The YOLOv7 model achieved the highest mAP@0.5:0.95 rate of 0.975. |
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