Offline Signature Verification Using Textural Descriptors

Hadjadj, Ismail; Gattal, Abdeljalil; Djeddi, Chawki; Ayad, Mouloud; Siddiqi, Imran; Abass, Faycel

doi:10.1007/978-3-030-31321-0_16

Cited by 8 publications

(7 citation statements)

References 35 publications

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“…Based on the application of different machine learning classification techniques: Bagging Tree, Random Forest (RF) and SVM, the system was tested on the UTSig data set, and the experimental results showed that SVM was better than other classifiers, with an accuracy rate of 94% [3]. Hadjadj et al used Local Ternary Patterns (LTP) and oriented Basic Image Features (oBIFs) texture descriptors to extract features [4], and projected the signature image into the feature space. When the signature to be tested was given, the authenticity was judged by combining the decision results of two SVM.…”

Section: Feature Extraction In Signaturesmentioning

confidence: 99%

Handwritten Signature Verification Method Based on Improved Combined Features

et al. 2021

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As a behavior feature, handwritten signatures are widely used in financial and administrative institutions. The appearance of forged signatures will cause great property losses to customers. This paper proposes a handwritten signature verification method based on improved combined features. According to advanced smart pen technology, when writing a signature, offline images and online data of the signature can be obtained in real time. It is the first time to realize the combination of offline and online. We extract the static and dynamic features of the signature and verify them with support vector machine (SVM) and dynamic time warping (DTW) respectively. We use a small number of samples during the training stage, which solves the problem of insufficient number of samples to a certain extent. We get two decision scores while getting the verification result. Finally, we propose a score fusion method based on accuracy (SF-A), which combines offline and online features through score fusion and utilize the complementarity among classifiers effectively. Experimental results show that using different numbers of training samples to conduct experiments on local data sets, the false acceptance rate (FAR) and false reject rate (FRR) obtained are better than the offline or online verification results.

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Section: Feature Extraction In Signaturesmentioning

confidence: 99%

Handwritten Signature Verification Method Based on Improved Combined Features

et al. 2021

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“…Five performance metrics-accuracy, precision, recall, F-score, and loss metrics-are used to evaluate the efficacy of our proposed methodology. According to (14), the percentage of true positive and true negative categorized points to all total points is known as accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Hadjadj et al [14] offered a method for determining whether a signature is authentic by using the textural information in the image of the signature. The local ternary patterns (LTP) and the orientated basic image features (oBIFs) are two textural descriptors that were utilized to describe the signature images.…”

Section: Related Workmentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

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<span>Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.</span>

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“…", The correct answer can only be "Information Technology", although the writers may write words using different cases, such as "information technology" and "Information technology". The nature of these exam papers makes it suitable to be used for short answer question assessment systems [19]. Figure 1 displays some examples of handwritten short answers.…”

Section: B Competition Datasetsmentioning

confidence: 99%

“…Neighbouring pixels (with reference to the central pixel) taking values between these thresholds are assigned with 0; value of 1 is assigned to the pixels with value greater than the upper threshold; and value of -1 otherwise. [19]: In this system, images are characterised using two textural descriptors, the Local Ternary Patterns (LTP) and the oriented basic image features (oBIFs).…”

Section: Ltp: This System Is Based On Local Ternary Pattern (Ltp)mentioning

confidence: 99%

ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020)

Das

Suwanwiwat

Pal

et al. 2020

2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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This paper describes the results of the competition on Short answer ASsessment and Thai student SIGnature and NameCOMponents Recognition and Verification (SASIGCOM 2020) in conjunction with the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR 2020). The competition was aimed to automate the evaluation process short answer-based examination and record the development and gain attention to such system. The proposed competition contains three elements which are short answer assessment (recognition and marking the answers to short-answer questions derived from examination papers), student name components (first and last names) and signature verification and recognition. Signatures and name components data were collected from 100 volunteers.For the Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. With Thai name components dataset, there are 30 genuine and 12 skilfully forged name components for each writer. There are 104 exam papers in the short answer assessment dataset, 52 of which were written with cursive handwriting; the rest of 52 papers were written with printed handwriting. The exam papers contain ten questions, and the answers to the questions were designed to be a few words per question. Three teams from distinguished labs submitted their systems. For short answer assessment, word spotting task was also performed. This paper analysed the results produced by their algorithms using a performance measure and define a way forward for this subject of research. Both the datasets, along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.

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Offline Signature Verification Using Textural Descriptors

Cited by 8 publications

References 35 publications

Handwritten Signature Verification Method Based on Improved Combined Features

Handwritten Signature Verification Method Based on Improved Combined Features

Signature verification based on proposed fast hyper deep neural network

ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents Recognition and Verification (SASIGCOM 2020)

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