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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+extract from https://data.mendeley.com/datasets/c5mfhr2xcz/1
+
+# Offline Handwritten Signature Database based on Age Annotation (OHSDA)
+
+Published: 27 February 2023
+Version 1
+DOI: 10.17632/c5mfhr2xcz.1
+
+Contributors:
+Sathish Kumar , Dr Shivanand Gornale
+
+
+## Description
+Handwritten signature analysis is the endeavoring research in many verification and recognition system problem. 
+As per our best of knowledge there is less number of publicly available datasets which have age class annotation 
+for signature. With this motivation, own dataset is created. The nature of this own offline handwritten 
+signature database is described below. 
+
+• In-house total of 6010 signatures were collected randomly in 601 healthy volunteers (330 males; 271 females; 
+Age range 18-50 years) 
+
+• From each individual 10 signatures acquired on a white A4 paper sheet using blue or black colored ball pen. 
+
+• To avoid geometrical variations, the papers with sample signatures have been scanned using the EPSON DS 1630 
+color scanner with a resolution of 300 DPI.
+
+• The signature samples consist of multilingual scripts of Kannada, Hindi, Marathi, and English.
+
+• The participants who had knowledge of English and other regional languages, have been educated about the 
+purpose of collection of signature samples.
+
+Male signatures age range will start from ‘male signatures 18-50 years’ and female signature will starts from 
+‘female signatures 18-50 years’.
+
+All Scanned Offline Signature samples are in .jpg format, the male signatures start with 'm' and female 
+signatures start with 'f'.
+
+ 
+
+Download All 109 MB
+
+Files
+
+Female Signatures 18-50 years
+
+Male Signatures 18-50 years
+Steps to reproduce
+Handwritten signatures are socially and legally accepted behavioral biometric data for authenticating the 
+documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life.
+
+However, it is observed that very less effort is done on age identification based on offline handwritten 
+signatures. These signatures contain multilingual texts. Therefore, to bridge this research gap, the proposed 
+database is used to identify writer’s age group from handwritten signatures of individuals.
+
+The signature samples were collected from various educational institutions and some village are for diversity 
+purpose.
+
+• In-house total of 6010 signatures were collected randomly in 601 healthy volunteers (330 males; 271 females; 
+Age range 18-50 years) 
+
+• From each individual 10 signatures acquired on a white A4 paper sheet using blue or black colored ball pen. 
+
+• To avoid geometrical variations, the papers with sample signatures have been scanned using the EPSON DS 1630 
+color scanner with a resolution of 300 DPI.
+
+• The signature samples consist of multilingual scripts of Kannada, Hindi, Marathi, and English.
+
+• The participants who had knowledge of English and other regional languages, have been educated about the 
+purpose of collection of signature samples.
+
+All participants gave written informed consent, and the study was approved by the institutional ethics 
+committee.
+ 
+
+Institutions
+Rani Channamma University
+Categories
+Biometrics, Age Diversity