Online Handwriting-Based Gender Recognition Using Statistical and Machine Learning Approaches-Reference-Cited by-同舟云学术

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Online Handwriting-Based Gender Recognition Using Statistical and Machine Learning Approaches

Published:2024 Issue: Volume:12 Page:93791-93801
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shin Jungpil¹^ORCID,Uchida Yuta²,Maniruzzaman Md.¹^ORCID,Hirooka Koki¹^ORCID,Megumi Akiko³^ORCID,Yasumura Akira⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan

2. Rakuten Group Inc., Tokyo, Japan

3. Department of Pediatrics and Child Health, Kurume University School of Medicine, Kurume, Japan

4. Graduate School of Social and Cultural Sciences, Kumamoto University, Chuo, Kumamoto, Japan

Funder

Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI), Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10587248.pdf?arnumber=10587248

Reference37 articles.

1. Individuality of Handwriting

2. Offline Script Identification from multilingual Indic-script documents: A state-of-the-art

3. Automatic analysis of handwriting for gender classification

4. Automatic gender detection using on-line and off-line information

5. Local descriptors to improve off-line handwriting-based gender prediction

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