Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks
Joint Authors
Sánchez, Angel
Morera, Ángel
Vélez, José Francisco
Moreno, Ana Belén
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-14
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics.
This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively.
Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts.
The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses.
Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones.
Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addressed before by other researchers.
Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems.
Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.
American Psychological Association (APA)
Morera, Ángel& Sánchez, Angel& Vélez, José Francisco& Moreno, Ana Belén. 2018. Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks. Complexity،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1133852
Modern Language Association (MLA)
Morera, Ángel…[et al.]. Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks. Complexity No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1133852
American Medical Association (AMA)
Morera, Ángel& Sánchez, Angel& Vélez, José Francisco& Moreno, Ana Belén. Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks. Complexity. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1133852
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133852