Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages
Recent advances in information technology and the need for more security have led to the rapid development of intelligent biometric identification systems. Recent studies have proven that handwriting of each person is unique and can be used as one of the authentication methods. There are many resear...
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| Format: | Article |
| Language: | fas |
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Semnan University
2021-01-01
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| Series: | مجله مدل سازی در مهندسی |
| Subjects: | |
| Online Access: | https://modelling.semnan.ac.ir/article_4843_cd1a91a1d7bab16606851dee111905ae.pdf |
| _version_ | 1797296541150478336 |
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| author | Mostafa Sabzekar Reyhane Khazaei Vahide Babaiyan Younes Akbari |
| author_facet | Mostafa Sabzekar Reyhane Khazaei Vahide Babaiyan Younes Akbari |
| author_sort | Mostafa Sabzekar |
| collection | DOAJ |
| description | Recent advances in information technology and the need for more security have led to the rapid development of intelligent biometric identification systems. Recent studies have proven that handwriting of each person is unique and can be used as one of the authentication methods. There are many researches in the literature for writer identification on a specific language. Unfortunately, there are no necessary data sets for this purpose. In this paper, for the first time, a handwritten data set of 300 persons in both Persian and English languages was collected. The main goal of this paper is to provide a model to identify the writer independent of the language written in Persian and English. After pre-processing stage, each person's handwriting is converted into blocks of a certain size called a texture. Then, using these textures, the desired features are extracted. In order to extract these features, first a two-dimensional wavelet transform is applied to each image and then, using the new algorithm for calculating the fractal dimension, which is used for the first time in this field, the feature vector is obtained. Finally, MLP neural networks are utilized for classification step. The performance of the proposed method is evaluated in different scenarios. |
| first_indexed | 2024-03-07T22:06:15Z |
| format | Article |
| id | doaj.art-77be8e01984f4137a26232a13d13ef03 |
| institution | Directory Open Access Journal |
| issn | 2008-4854 2783-2538 |
| language | fas |
| last_indexed | 2024-03-07T22:06:15Z |
| publishDate | 2021-01-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj.art-77be8e01984f4137a26232a13d13ef032024-02-23T19:08:16ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382021-01-01186311310.22075/jme.2021.21786.19934843Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languagesMostafa Sabzekar0Reyhane Khazaei1Vahide Babaiyan2Younes Akbari3Department of Computer Engineering, Birjand University of Technology, Birjand, IranDepartment of Computer Engineering, Islamic Azad University, BirjandDepartment of Computer Engineering, Birjand University of Technology, Birjand, IranDepartment of Computer Engineering, Qatar University, QatarRecent advances in information technology and the need for more security have led to the rapid development of intelligent biometric identification systems. Recent studies have proven that handwriting of each person is unique and can be used as one of the authentication methods. There are many researches in the literature for writer identification on a specific language. Unfortunately, there are no necessary data sets for this purpose. In this paper, for the first time, a handwritten data set of 300 persons in both Persian and English languages was collected. The main goal of this paper is to provide a model to identify the writer independent of the language written in Persian and English. After pre-processing stage, each person's handwriting is converted into blocks of a certain size called a texture. Then, using these textures, the desired features are extracted. In order to extract these features, first a two-dimensional wavelet transform is applied to each image and then, using the new algorithm for calculating the fractal dimension, which is used for the first time in this field, the feature vector is obtained. Finally, MLP neural networks are utilized for classification step. The performance of the proposed method is evaluated in different scenarios.https://modelling.semnan.ac.ir/article_4843_cd1a91a1d7bab16606851dee111905ae.pdfwriter identificationlanguage-independent handwriting recognitioncreating textureswavelet transformfractal dimension |
| spellingShingle | Mostafa Sabzekar Reyhane Khazaei Vahide Babaiyan Younes Akbari Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages مجله مدل سازی در مهندسی writer identification language-independent handwriting recognition creating textures wavelet transform fractal dimension |
| title | Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages |
| title_full | Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages |
| title_fullStr | Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages |
| title_full_unstemmed | Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages |
| title_short | Script independent offline writer identification from handwriting samples based on texture using wavelet transform in Persian-English languages |
| title_sort | script independent offline writer identification from handwriting samples based on texture using wavelet transform in persian english languages |
| topic | writer identification language-independent handwriting recognition creating textures wavelet transform fractal dimension |
| url | https://modelling.semnan.ac.ir/article_4843_cd1a91a1d7bab16606851dee111905ae.pdf |
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