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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.
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): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 108
Country: Bangladesh
Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision
Authors: Tansin Jahan, Md. Shahriar Anwar, Dr. S. M. Abdullah Al-Mamun (PhD/Dr. count: 1)
View Count (all-time): 281
Total Views (Real + Logic): 8212
Total Downloads (simulated): 2131
Publish Date: 2015 07, Wed
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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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