Abstract
Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer’s gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans.
Original language | English |
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Title of host publication | Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings |
Editors | Yannis Manolopoulos, Barbara Hammer, Vera Kurkova, Lazaros Iliadis, Ilias Maglogiannis |
Publisher | Springer Verlag |
Pages | 613-621 |
Number of pages | 9 |
ISBN (Print) | 9783030014230 |
DOIs | |
State | Published - 2018 |
Event | 27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece Duration: 4 Oct 2018 → 7 Oct 2018 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11141 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 27th International Conference on Artificial Neural Networks, ICANN 2018 |
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Country/Territory | Greece |
City | Rhodes |
Period | 4/10/18 → 7/10/18 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2018.
Keywords
- Convolutional neural network
- Deep neural network
- Gender classification
- HEBIU handwriting dataset
- Offline handwriting