An evaluation on offline signature verification using artificial neural network approach

Khalifa, Othman O.; Alam, M. K.; Abdalla, Aisha H.

doi:10.1109/icceee.2013.6633964

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The signature itself only takes up a small part of the image and most of the information is useless background information, which will have a significant impact on training. Khalifa et al [4] transformed the original RGB image into a gray scale image. This processing can minimise the influence of background information on training and classification, allowing the training model to pay more attention to the stroke part of the image rather than other information such as background and colour.…”

Section: Preprocessingmentioning

confidence: 99%

Research advanced in offline handwritten signature verification

Guo,

Li,

2023

ACE

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Biometrics technology has penetrated into various fields of our lives that require authentication and verification, such as criminal investigation, check processing and legal procedures, which is a focus of debate in the scientific research community. As a biometric feature that is easy to obtain, verification of handwritten signatures has attracted great interest in the past several decades. Although the online handwritten signature verification (OnHSV) system can obtain more information and has a higher accuracy rate, the offline handwritten signature verification (OfHSV) system remains the focus of research, since most signatures we can obtain are offline. In this paper, according to the difference of steps of OfHSV, we classify and introduce the advanced research work of OfHSV from four aspects: datasets, preprocessing techniques, feature extraction methods, and neural network structures. Additionally, we introduced the background of signature verification system and the experimental results of existing technologies. Finally, we presented some notable challenges, leading the reader to current trends and future directions in this field.

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Section: Preprocessingmentioning

confidence: 99%

Research advanced in offline handwritten signature verification

Guo,

Li,

2023

ACE

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“…Othman o-khalifa et al [2] has reviewed offline signature verification schemes in their paper. They have considered the Artificial Neural Network Technique, and have compared various offline signature verification approaches and their issues.…”

Section: Related Workmentioning

confidence: 99%

“…Signature, from the Latin word "Signare" meaning "Sign" is a stylized handwritten representation of a person's name or an identification mark that a person writes on documents/texts. For many centuries, signatures have been used as an important element in authentication of any person's identity, who is signing the document [2]. The unique characteristics of a person's signature represent the person's identity and the person's consent for the terms of the document/text.…”

Section: Introductionmentioning

confidence: 99%

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

Prakash

Sharma²

2014

IJ-AI

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Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, Ist and IInd order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.

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“…In the online system, signature data obtained from an electronic tablet to obtain dynamic information about writing activities such as write speed, pen pressure when writing, and the number of strokes [4]. While the system is offline, the signature is written on paper and then converted to digital image format with the help of a camera or scanner [5].…”

Section: Introductionmentioning

confidence: 99%

Component-connected Feature for Signature Identification

Umniati

Mutiara

Kusuma

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-A signature is the oldest security techniques to verify the identification of a person. This is due to every person has a different signature, and each signature has the characteristic physiological and behavior. There are two kinds of signature such as offline and online signatures used to verify someone identity. Offline signatures were used in this study because offline signature does not have dynamic features such as an online signature. This study proposed an identification system of offline signature by using k-NN based on the features that were stored in the database. The proposed identification system consists of preprocessing, feature extraction and verification stages. We collected the data samples from 10 persons. Each person wrote ten signatures. Total data was 100 signatures. The first stage used in this study was preprocessing such as noise removal, binarization, skeleton, and cropping. The second stage was feature extraction. Feature extraction had some vital information such as height-width ratio, the ratio of the density of signatures, edge distance ratio, the ratio of the number and proximity of the column, and the number of connected components in the signature. That information was stored in a separate database. We separated ten signatures of each person into six signatures as data sample and four signature as test data. We verified 40 signatures of test data from 10 persons using k-NN. It is shown that from 40 signatures used in our test data, 28 signatures were correctly identified and 12 signatures belong to others.

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An evaluation on offline signature verification using artificial neural network approach

Cited by 7 publications

References 10 publications

Research advanced in offline handwritten signature verification

Research advanced in offline handwritten signature verification

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

Component-connected Feature for Signature Identification

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