Optimized detection of Urdu signatures in real-world images using YOLO v7
Hussain, Muzammal (Government College University Faisalabad (Pakistan))
Rafiq, Muhammad Ahsan (Government College University Faisalabad (Pakistan))

Fecha:	2026
Resumen:	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.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Computer vision ; Image analysis ; Signature detection ; Urdusig ; Deep learning ; Handwritten signature ; Yolov7
Publicado en:	ELCVIA, Vol. 25, Num. 1 (2026) , p. 83-97 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/2267
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000007327
DOI: 10.5565/rev/elcvia.2267

15 p, 21.3 MB

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