license:cc-by-4.0 size_categories:1K<n<10K format:imagefolder modality:image library:datasets library:mlcroissant region:us
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license: cc-by-4.0
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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
