Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks
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...
Saved in:
Main Authors: | Ángel Morera, Ángel Sánchez, José Francisco Vélez, Ana Belén Moreno |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2018-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2018/3891624 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Survey of Offline Handwriting Signature Verification
by: Jihad Majeed Nori, et al.
Published: (2025-01-01) -
Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network
by: Muhammad Iqbal, et al.
Published: (2024-10-01) -
Early handwriting development: a longitudinal perspective on handwriting time, legibility, and spelling
by: Lidia Truxius, et al.
Published: (2025-01-01) -
Associations between handedness and brain functional connectivity patterns in children
by: Dardo Tomasi, et al.
Published: (2024-03-01) -
Family History of Handedness and Language Problems in Mexican Reading-Disabled Children
by: E. Matute, et al.
Published: (1996-01-01)