A Study on Preprocessing and Feature Extraction in offline Handwritten Signatures

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Tansin Jahan
Tansin Jahan
σ
Md. Shahriar Anwar
Md. Shahriar Anwar
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Dr. S. M. Abdullah Al-Mamun
Dr. S. M. Abdullah Al-Mamun

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A Study on Preprocessing and Feature Extraction in offline Handwritten Signatures

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Abstract

In offline handwritten signature verification process, preprocessing of the signature is the very fast and most essential part. In some cases the raw signature can include extra pixel known as noises or may not be in proper form where preprocessing is mandatory. If a signature is preprocessed correctly, it leads to a better result for both signature matching and forgery detection. Pre-processing includes binarization, noise removal, thinning, orientation etc. Many experiments and techniques have already been proposed for implementing these processes and some of them have shown exclusive and spectacular results. Regarding to this situation we have studied several preprocessing steps, signature features, feature detectors and also implemented some of them using MATLAB software. We have studied several image processing algorithms, and proposed an algorithm to correct the alignment of the input signature which can be used at the preprocessing stage to achieve better results in the signature detection process. We have tried to find a baseline of the handwritten signature and align it with respect to the baseline.

References

10 Cites in Article
  1. Vamsi Madasu,Brian Lovell (2008). An Automatic Off-Line Signature Verification and Forgery Detection System.
  2. A Gulzar,Mohammad Khuwaja,Laghari Offline Handwritten Signature Recognition.
  3. Phalguni Dakshina Ranjan Kisku,& Gupta,Kanta Jamuna,Sing (2010). Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory.
  4. Bassam Al-Mahadeen,Mokhled Altarawneh,H Islam,Altarawneh (2010). Signature Region of Interest using Auto cropping.
  5. Ravneet Gill,Maninder Singh (2012). Statistical Features Based Off Line Signature Verification System using Image Processing Techniques.
  6. Priya Metri,& Ashwinder,Kaur (2011). Handwritten Signature Verification using Instance Based Learning.
  7. Philippe Dr,Cattin (2012). Image Restoration: Introduction to Signal and Image Processing.
  8. Tal Steinherz,David Doermann,Ehud Rivlin,Nathan Intrator (2009). Offline Loop Investigation for Handwriting Analysis.
  9. Debnath Bhattachar,Samir Bandyopadh,Poulami Das,Debashis Ganguly,Swarnendu Mukherjee (2008). Statistical Approach for Offline Handwritten Signature Verification.
  10. Suhail Odeh,Manal Khalil (2011). Apply Multi-Layer Perceptrons Neural Network for Off-line signature verification and recognition.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tansin Jahan. 2015. \u201cA Study on Preprocessing and Feature Extraction in offline Handwritten Signatures\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 15 (GJCST Volume 15 Issue F2): .

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Issue Cover
GJCST Volume 15 Issue F2
Pg. 21- 25
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-F Classification: I.3.2
Version of record

v1.2

Issue date

July 15, 2015

Language
en
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In offline handwritten signature verification process, preprocessing of the signature is the very fast and most essential part. In some cases the raw signature can include extra pixel known as noises or may not be in proper form where preprocessing is mandatory. If a signature is preprocessed correctly, it leads to a better result for both signature matching and forgery detection. Pre-processing includes binarization, noise removal, thinning, orientation etc. Many experiments and techniques have already been proposed for implementing these processes and some of them have shown exclusive and spectacular results. Regarding to this situation we have studied several preprocessing steps, signature features, feature detectors and also implemented some of them using MATLAB software. We have studied several image processing algorithms, and proposed an algorithm to correct the alignment of the input signature which can be used at the preprocessing stage to achieve better results in the signature detection process. We have tried to find a baseline of the handwritten signature and align it with respect to the baseline.

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A Study on Preprocessing and Feature Extraction in offline Handwritten Signatures

Tansin Jahan
Tansin Jahan
Md. Shahriar Anwar
Md. Shahriar Anwar
Dr. S. M. Abdullah Al-Mamun
Dr. S. M. Abdullah Al-Mamun

Research Journals