Signature Detection and Identification Algorithm with CNN, Numpy and OpenCV

Afanasyeva, Zhanna S.; Afanasyev, Alexander D.

doi:10.1007/978-3-030-63319-6_43

Cited by 2 publications

(1 citation statement)

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“…Afanasyeva and Afanasyev [12] describe a straightforward signature detection approach and its subsequent signature recognition based on a convolution neural network using a deep learning model for image processing. A binary classification has been carried out to predict text or signature and signature classifications utilizing solutions to the picture recognition problem and to transfer the signature-containing region to the trained model after being extracted.…”

Section: Introductionmentioning

confidence: 99%

Handwritten signature identification based on MobileNets model and support vector machine classifier

Mohammed¹,

Mahdi²,

Kadhm³

2023

Bulletin EEI

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Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.

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Section: Introductionmentioning

confidence: 99%