Handwritten Texts for Personality Identification Using Convolutional Neural Networks

José E. Valdez-Rodríguez; Hiram Calvo; Edgardo M. Felipe-Riverón

doi:10.1007/978-3-030-05792-3_13

Handwritten Texts for Personality Identification Using Convolutional Neural Networks

José E. Valdez-Rodríguez, Hiram Calvo, Edgardo M. Felipe-Riverón

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

The task consists in estimating the personality traits of users from their handwritten texts. To classify them, we use the scanned image of the subject hand-written essay divided in patches and we propose in this work an architecture based on a Convolutional Neural Network (CNN) as classifier. The original dataset consists of 418 images in color, from which we obtained 216 patches of each image in grayscale and then we binarized them resulting in approximately 90,000 images. The CNN consists of five convolutional layers to extract features of the patches and three fully connected layers to perform the classification.

Original language	English
Title of host publication	Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNA, Revised Selected Papers
Editors	Nicolas Sidère, Zhaoxiang Zhang, Yingli Tian, Hugo Jair Escalante, David Suter, Alexandra Branzan Albu
Publisher	Springer Verlag
Pages	140-145
Number of pages	6
ISBN (Print)	9783030057916
DOIs	https://doi.org/10.1007/978-3-030-05792-3_13
State	Published - 2019
Event	3rd International Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China Duration: 20 Aug 2018 → 24 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11188 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International Conference on Pattern Recognition, ICPR 2018
Country/Territory	China
City	Beijing
Period	20/08/18 → 24/08/18

Keywords

CNN
Handwritten text
Image processing
Personality traits

Access to Document

10.1007/978-3-030-05792-3_13

Cite this

Valdez-Rodríguez, J. E., Calvo, H., & Felipe-Riverón, E. M. (2019). Handwritten Texts for Personality Identification Using Convolutional Neural Networks. In N. Sidère, Z. Zhang, Y. Tian, H. Jair Escalante, D. Suter, & A. Branzan Albu (Eds.), Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNA, Revised Selected Papers (pp. 140-145). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11188 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-05792-3_13

Valdez-Rodríguez, José E. ; Calvo, Hiram ; Felipe-Riverón, Edgardo M. / Handwritten Texts for Personality Identification Using Convolutional Neural Networks. Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNA, Revised Selected Papers. editor / Nicolas Sidère ; Zhaoxiang Zhang ; Yingli Tian ; Hugo Jair Escalante ; David Suter ; Alexandra Branzan Albu. Springer Verlag, 2019. pp. 140-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Handwritten Texts for Personality Identification Using Convolutional Neural Networks",

abstract = "The task consists in estimating the personality traits of users from their handwritten texts. To classify them, we use the scanned image of the subject hand-written essay divided in patches and we propose in this work an architecture based on a Convolutional Neural Network (CNN) as classifier. The original dataset consists of 418 images in color, from which we obtained 216 patches of each image in grayscale and then we binarized them resulting in approximately 90,000 images. The CNN consists of five convolutional layers to extract features of the patches and three fully connected layers to perform the classification.",

keywords = "CNN, Handwritten text, Image processing, Personality traits",

author = "Valdez-Rodr{\'i}guez, {Jos{\'e} E.} and Hiram Calvo and Felipe-River{\'o}n, {Edgardo M.}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 3rd International Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International Conference on Pattern Recognition, ICPR 2018 ; Conference date: 20-08-2018 Through 24-08-2018",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Valdez-Rodríguez, JE , Calvo, H & Felipe-Riverón, EM 2019, Handwritten Texts for Personality Identification Using Convolutional Neural Networks. in N Sidère, Z Zhang, Y Tian, H Jair Escalante, D Suter & A Branzan Albu (eds), Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNA, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11188 LNCS, Springer Verlag, pp. 140-145, 3rd International Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 20/08/18. https://doi.org/10.1007/978-3-030-05792-3_13

Handwritten Texts for Personality Identification Using Convolutional Neural Networks. / Valdez-Rodríguez, José E.; Calvo, Hiram ; Felipe-Riverón, Edgardo M.
Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNA, Revised Selected Papers. ed. / Nicolas Sidère; Zhaoxiang Zhang; Yingli Tian; Hugo Jair Escalante; David Suter; Alexandra Branzan Albu. Springer Verlag, 2019. p. 140-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11188 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Handwritten Texts for Personality Identification Using Convolutional Neural Networks

AU - Valdez-Rodríguez, José E.

AU - Calvo, Hiram

AU - Felipe-Riverón, Edgardo M.

PY - 2019

Y1 - 2019

N2 - The task consists in estimating the personality traits of users from their handwritten texts. To classify them, we use the scanned image of the subject hand-written essay divided in patches and we propose in this work an architecture based on a Convolutional Neural Network (CNN) as classifier. The original dataset consists of 418 images in color, from which we obtained 216 patches of each image in grayscale and then we binarized them resulting in approximately 90,000 images. The CNN consists of five convolutional layers to extract features of the patches and three fully connected layers to perform the classification.

AB - The task consists in estimating the personality traits of users from their handwritten texts. To classify them, we use the scanned image of the subject hand-written essay divided in patches and we propose in this work an architecture based on a Convolutional Neural Network (CNN) as classifier. The original dataset consists of 418 images in color, from which we obtained 216 patches of each image in grayscale and then we binarized them resulting in approximately 90,000 images. The CNN consists of five convolutional layers to extract features of the patches and three fully connected layers to perform the classification.

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KW - Handwritten text

KW - Image processing

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A2 - Tian, Yingli

A2 - Jair Escalante, Hugo

A2 - Suter, David

A2 - Branzan Albu, Alexandra

PB - Springer Verlag

T2 - 3rd International Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International Conference on Pattern Recognition, ICPR 2018

Y2 - 20 August 2018 through 24 August 2018

ER -

Valdez-Rodríguez JE , Calvo H , Felipe-Riverón EM. Handwritten Texts for Personality Identification Using Convolutional Neural Networks. In Sidère N, Zhang Z, Tian Y, Jair Escalante H, Suter D, Branzan Albu A, editors, Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNA, Revised Selected Papers. Springer Verlag. 2019. p. 140-145. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-05792-3_13

Handwritten Texts for Personality Identification Using Convolutional Neural Networks

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