Signature verification: A study

Mushtaq, Saba; Mir, Ajaz Hussain

doi:10.1109/iccct.2013.6749637

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…The person who made the signature with their vogue regarding that he has not any data of the orthography and knowledge. This will be through with observant the signature closely for the associate degree within enough time [16].…”

Section: Unskilled Forgerymentioning

confidence: 95%

Signature Recognition Based on Discrete Wavelet Transform

Muhamad

Al-Ani

2019

UHD J SCI TECH

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Personal identification is an actively developing area of research. Human signature is a vital biometric attribute which can be used to authenticate human identity. There are many approaches to recognize signature with a lot of researches. The aim of this research is to introduce an efficient approach for signature recognition. This approach starts with the process the acquired signatures and stores these signatures in the database to be ready for verification. The collection of signature data based on collecting samples of 10 people and 10 signatures for each person through traditional ink stamp method. These signatures are digitized to be ready for processing. Many steps are applied to the acquired images to perform the pre-processing stage. The proposed approach based on discrete wavelet transforms to extract significant features from each signature image. Pre-processing is applied at the beginning of this approach to avoid any unwanted noise. This approach consists of many steps: Data acquisition, pre-processing, signature registration, and feature extraction. High recognition rate results (100%) are obtained through applying this approach.

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Section: Unskilled Forgerymentioning

confidence: 95%

Signature Recognition Based on Discrete Wavelet Transform

Muhamad

Al-Ani

2019

UHD J SCI TECH

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“…Mushtaq and Mir [7] presented a comparison between the results of various writer-dependent (WD) signature verification systems. Kumar and Bhatia [8] presented a survey paper comparing both Writer-Dependent (WD) and Writer-Independent (WI) handwritten signature verification systems.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

Hashim

Ahmed

Alkhayyat

2022

Scientific Programming

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Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields.

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“…These movements can change according to many factors such as: tiredness, mood, etc. [1]. The signature has a high acceptance by the public and specialized applications such as: validating paper documents and use in banking transactions [2].…”

Section: Introductionmentioning

confidence: 99%

Offline Handwritten Signature Verification Based on Local Ridges Features and Haar Wavelet Transform

Abdul-Haleem

2022

eijs

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Multiple applications use offline handwritten signatures for human verification. This fact increases the need for building a computerized system for signature recognition and verification schemes to ensure the highest possible level of security from counterfeit signatures. This research is devoted to developing a system for offline signature verification based on a combination of local ridge features and other features obtained from applying two-level Haar wavelet transform. The proposed system involves many preprocessing steps that include a group of image processing techniques (including: many enhancement techniques, region of interest allocation, converting to a binary image, and Thinning). In feature extraction and analysis stages, a combination of local ridge features and other features obtained from the details of Haar wavelet subbands are extracted. Each wavelet sub-band image is fragmented into blocks with overlap and then the local features and wavelet energies are extracted from each block. Experiments were performed using a database of 600 signature prints collected from 100 persons, (i.e., 6 samples per person). The recognition accuracy of the system was the optimum (100%) using two decomposition levels. For verification purposes, the False Reject Rate for the system was (0.025%) while False Acceptable Rate was (0.03%) respectively.

show abstract

Signature verification: A study

Cited by 13 publications

References 31 publications

Signature Recognition Based on Discrete Wavelet Transform

Signature Recognition Based on Discrete Wavelet Transform

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

Offline Handwritten Signature Verification Based on Local Ridges Features and Haar Wavelet Transform

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