Handwritten Signature Verification using TensorFlow

Rahul, D.; Lather, J. S.

doi:10.1109/rteict42901.2018.9012273

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The result of applying the WT for the input image is shown in Figure 3. Besides, the second step of the proposed preprocessing stage is image scaling [21]. Reducing the input handwritten signature image size makes the computation timeless.…”

Section: Preprocessingmentioning

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an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing.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: Preprocessingmentioning

confidence: 99%

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

Mohammed¹,

Mahdi²,

Kadhm³

2023

Bulletin EEI

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“…So, it is not stable and does not convenient for VI people. The researchers in [8][9][10] described a solution based on Convolutional Neural Network (CNN) using the TensorFlow library. The model is prepared with a dataset of signatures.…”

Section: Related Workmentioning

confidence: 99%

A Secured Digital Handwritten Signature Prototype for Visually Impaired People

Taha¹,

Selim²,

Yousry³

2020

IJIES

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No doubt, Visually Impaired (VI) have trouble signing documents. They cannot identify their signatures and have their identification verified. Hence, some people may impersonate their identity. They may imitate their signatures in formal documents like contracts, money checks, and other vital documents, mainly in the governmental institutions. No clarified method can maintain these signatures. This paper presents a new prototype for securing VI people's signature using encryption techniques and data hiding methods. The proposed prototype uses the Least Significant Bit (LSB) algorithm for hiding some information, including the name of VI, the date, and the time of the signature. These data are encrypted using an improved, chaotic algorithm with a linear feedback shift register approach. Moreover, a QR code for each VI person is created and associated with the document. Both the signature and the QR code can be used to secure and validate the VI signature for a given document. A real signature dataset is constructed for evaluating the proposed prototype. The experimental results with respect to the real dataset and the Spanish signature dataset (MCYT-100) proved that the superiority of the proposed prototype.

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Signature Detection and Identification Algorithm with CNN, Numpy and OpenCV

Afanasyeva

Afanasyev

2020

Software Engineering Perspectives in Intelligent Systems

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Handwritten Signature Verification using TensorFlow

Cited by 6 publications

References 11 publications

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

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

A Secured Digital Handwritten Signature Prototype for Visually Impaired People

Signature Detection and Identification Algorithm with CNN, Numpy and OpenCV

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