A New Text-Independent GMM Writer Identification System Applied to Arabic Handwriting

2014 ◽  
Author(s):  
Fouad Slimane ◽  
Volker Margner
Keyword(s):  
Identification System ◽  
Writer Identification

Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM

2019 ◽  
Vol 24 (13) ◽  
pp. 10111-10122 ◽  
Author(s):  
Shaveta Dargan ◽  
Munish Kumar ◽  
Anupam Garg ◽  
Kutub Thakur
Keyword(s):  
Identification System ◽  
Writer Identification

scholarly journals DeepWriterID: An End-to-End Online Text-Independent Writer Identification System

2016 ◽  
Vol 31 (2) ◽  
pp. 45-53 ◽  
Author(s):  
Weixin Yang ◽  
Lianwen Jin ◽  
Manfei Liu
Keyword(s):  
Identification System ◽  
Writer Identification ◽  
End To End

scholarly journals A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
M. F. Mridha ◽  
Abu Quwsar Ohi ◽  
Jungpil Shin ◽  
Muhammad Mohsin Kabir ◽  
Muhammad Mostafa Monowar ◽  
...  
Keyword(s):  
Identification System ◽  
Writer Identification ◽  
Indic Scripts

BIOMETRIC WRITER IDENTIFICATION: FEATURE ANALYSIS AND CLASSIFICATION

2006 ◽  
Vol 20 (04) ◽  
pp. 483-503 ◽  
Author(s):  
JOULIA CHAPRAN
Keyword(s):  
High Performance ◽  
Handwriting Recognition ◽  
Personal Identification ◽  
Small Scale ◽  
Identification System ◽  
Writer Identification ◽  
Dynamic Features ◽  
Digital Writing ◽  
The Subject ◽  
New Feature

This paper describes an efficient biometric writer identification system that can be used in a resource contained embedded environment. Writer identification (personal identification by general handwriting) is a relatively new area of handwriting research when compared to the handwriting recognition or signature verification areas. This work is aimed at exploring only small-scale handwriting samples, especially single handwritten words. A database of such samples has been collected dynamically using a digital writing tablet and a force sensitive pen. The dynamic approach adapted in this area is largely new, and only a few papers exist on the subject. Mainly dynamic features of handwriting connected with the writing process itself are considered, although some static features are also used. It is also shown how simple parameters can be used in embedded writer identification devices, and whether they can perform adequately in this type of application. A new feature selection algorithm based on likeness coefficients is proposed. The classifiers used are Minimum-Distance Classifier, Bayes Classifier, and finally their serial combination. The efficiency of this approach and its high performance together show the reasons for employing it in embedded biometric identification devices.

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