Off-line arabic signature recognition and verification

Ismail, Mohamed A.; Gad, S. A.

doi:10.1016/s0031-3203(99)00047-3

Cited by 82 publications

(49 citation statements)

References 18 publications

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“…Automatic signature verification is an established and very active research field [27][35] [7] with important applications to the validation of checks and other financial documents. Due to the demonstrated practical applications of signature verification, different techniques have already been applied: fuzzy logic [21], geometric features [15] [20], global characteristics [38], genetic algorithms [41], neural networks [4][5] [45], Hidden Markov Models [10], etc. In comparison, automatic signature recognition has received less attention, despite the potential applications that could use the signature as an identification tool [32] [33].…”

Section: Introductionmentioning

confidence: 99%

“…For example, an automated signature recognition system could provide a company with a unique technique for validating the identity of each individual accessing to certain securitysensitive facilities [28]. Other potential signature recognition applications are in law-enforcement applications, where the identification of perpetrators is a fundamental requirement of the solution, and in the analysis of some historical documents [21]. Some previous works in the area of automatic signature recognition are: Ammar et al [2] that uses a hierarchical scheme of signature descriptors to identify a test signature; Han and Sethi [19], which considers a set of geometric and topologic features to map a signature image into two string of finite symbols; Pavlidis et al [34], which proposes the application of active deformable models for approximating the external shape of a signature; and Riba et al [39], that compares different statistical methods, using a feature extraction preprocessing, to carry out the recognition of signatures.…”

Section: Introductionmentioning

confidence: 99%

“…Apparently, the signature recognition problem looks more complex than the signature verification problem, and relatively little research effort has been focused on automatic signature recognition. In Ismail and Gad's work [21], signature recognition and verification are treated as two separate and consecutive stages, where successful verification is highly dependent on successful recognition. Pavlidis et al [34] state that it would be of great value an intelligent signature identification system, which should be capable of arriving at a decision (recognition and verification) based only on the signature of the user.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

109

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Abstract. -The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification despite its potential applications for accessing security-sensitive facilities and for processing certain legal and historical documents. This paper presents an efficient off-line human signature recognition system based on Support Vector Machines (SVM) and compares its performance with a traditional classification technique, Multi-Layer Perceptrons (MLP). In both cases we propose two approaches to the problem: (1) construct each feature vector using a set of global geometric and moment-based characteristics from each signature and (2) construct the feature vector using the bitmap of the corresponding signature. We also present a mechanism to capture the intrapersonal variability of each user using just one original signature. Our results empirically show that SVM, which achieves up to 71% correct recognition rate, outperforms MLP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

109

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“…The corner line features described in [2] and modified corner line features were extracted in this paper.…”

Section: Feature Extractionmentioning

confidence: 99%

“…An off-line Arabic signature recognition and verification system was proposed in [2]. Its recognition phase was used the multi-stage classifier and a combination of global and local based features.…”

Section: Introductionmentioning

confidence: 99%

Uyghur Off-line Signature Verification Based on Modified Corner Line Features

Ubul¹,

Yibulayin²,

Mahpirat³

2017

dtcse

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Abstract.A modified corner line features based off-line signature verification method proposed for Uyghur handwritten signature in this paper. The signature images were preprocessed according to the nature of Uyghur signature. Then 3 types of corner line features and modified corner curve features were extracted separately. Experiments were performed using Euclidean distance classifier and non-linear SVM classifier for Uyghur signature samples from 150 genuine signatures, 72 random and skilled forgeries are selected from our Uyghur handwritten signature database. Experiments indicate that the MCLF-48 with training 75 samples has obtained 2.16% of FRR and 2.27% of FAR with none-linear SVM classifier. It was concluded that modified corner line features based verification method can capture the nature of Uyghur signature and its writing style more efficiently.

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New concepts for cluster construction and similarity measurement

2021

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In this study we introduce two new concepts: (1) a new approach to construct clusters and (2) a methodology to compute similarities between numerical vectors based on clusters. The new approach to construct clusters is based on a variable‐distance threshold. There are several important domains where using commonly utilized fixed distance threshold clustering method might create clusters contradicting human expert reasoning. The domains where variable‐distance threshold clustering is more suitable are discussed and explained. In addition, we introduce a new concept for computing similarities between two numerical vectors, based on a membership in corresponding clusters. Such a concept constitutes an appropriate tool under greater degree of uncertainty where model structure is vague, and data are unreliable. First, we describe the procedure for construction of variable‐distance threshold clusters. Then we provide several models for computing the similarity between the two numerical vectors based on clusters. Several examples are included to illustrate the practical application of the models.

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Off-line arabic signature recognition and verification

Cited by 82 publications

References 18 publications

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Uyghur Off-line Signature Verification Based on Modified Corner Line Features

New concepts for cluster construction and similarity measurement

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