Off-line Uyghur signature recognition based on modified grid information features

Ubul, Kurban; Adler, Andy; Abliz, Gulirana; Maimaitijiang, Yasheng; Hamdulla, Askar

doi:10.1109/isspa.2012.6310446

Cited by 16 publications

(8 citation statements)

References 2 publications

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“…Classification is a patent and important technique that is applicable to numerous fields and domains [14]. There are several classification algorithms but authors have concentrated on those learning methods of classification which are available in both WEKA and RapidMiner DM tools and are capable of processing chosen dataset.…”

Section: Methodsmentioning

confidence: 99%

Performance Analysis of Classification Learning Methods on Large Dataset using two Data Mining Tools

Ali¹,

Szabist²,

Ali³

2015

JISR-C

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Data is increasing day to day thus, processing this data and selection of right method and tool is really a big problem. Computer scientists are processing and analysing data on different machine learning methods using various Data Mining tools to get the high accuracy of results and minimum time for building of Model. There are several data analysis and processing tools like WEKA, RapidMiner, Keel, and etc. available for the purpose of processing, analysis, modelling and etc. Still no single tool is perfect or nominated for data processing and analysis. In this concern, the authors present here a comparative and analytical research study on the performance of different classification machine learning algorithms like Naïve Bayes, KNN, IBK, Random Forest, C4.5, J48 and Data Mining tools which are WEKA and RapidMiner on a large datasets to evaluate their performance and analytical results with low cost of error. The data set Adult Income is taken from UCI Data repository for this research study. The significance and aim of this study is to evaluate and assess the range of performance of different machine learning methods and two diverse data mining tools on dissimilar datasets. The result of each classification method and Data mining tool is analysed and presented in the end.

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Section: Methodsmentioning

confidence: 99%

Performance Analysis of Classification Learning Methods on Large Dataset using two Data Mining Tools

Ali¹,

Szabist²,

Ali³

2015

JISR-C

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“…The genuine signatures used in here were selected from Uyghur signatures datasets collected in [9]. For forgery signatures collection, different 75 native Uyghur people selected respect with gender and ages.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Since pre-processing is not key point in this paper and it is adapted to the nature of Uyghur signature in our previous work [8,9]. So it is taken same preprocessing methods for Uyghur signature in this paper explained in [8,9], and it is omitted to describe here.…”

Section: Pre-processingmentioning

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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“…al. presented in [6] an off-line signature recognition system using three classifiers and also feature extraction. The used classifiers are the Euclidean distance (ED) classifier, the K-nearest neighbor (K-NN) classifier and the Bayes classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Numerous types of features have been used in the researches such as: the proportion factor which describes the relation between the width and height of the signature, the vertical and horizontal histogram which represents the projection of vertical and horizontal signature pixels, the center of gravity which is represented by a point where two orthogonal lines cross through it and the number of signature pixels (black pixels) within the resulting quarters are the same [2,5], the normalized area of a bounded signature which is the ratio of the signature area (the number of pixels encompassing the signature) to the area of the bounding box [5] and the grid features in which the signature is divided into rectangles then the area for each small rectangle is calculated [6].…”

Section: B Feature Extractionmentioning

confidence: 99%

Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction

Al-Saegh¹

2015

IJEEE

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The problem of automatic signature recognition and verification has been extensively investigated due to the vitality of this field of research. Handwritten signatures are broadly used in daily life as a secure way for personal identification. In this paper a novel approach is proposed for handwritten signature recognition in an off-line environment based on Weightless Neural Network (WNN) and feature extraction. This type of neural networks (NN) is characterized by its simplicity in design and implementation. Whereas no weights, transfer functions and multipliers are required. Implementing the WNN needs only Random Access Memory (RAM) slices. Moreover, the whole process of training can be accomplished with few numbers of training samples and by presenting them once to the neural network. Employing the proposed approach in signature recognition area yields promising results with rates of 99.67% and 99.55% for recognition of signatures that the network has trained on and rejection of signatures that the network .has not trained on, respectively

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Off-line Uyghur signature recognition based on modified grid information features

Abstract: Many

Cited by 16 publications

References 2 publications

Performance Analysis of Classification Learning Methods on Large Dataset using two Data Mining Tools

Performance Analysis of Classification Learning Methods on Large Dataset using two Data Mining Tools

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

Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction

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