Handwritten Texts for Personality Identification Using Convolutional Neural Networks

  • José E. Valdez-Rodríguez
  • Hiram Calvo
  • Edgardo M. Felipe-Riverón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11188)


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.


Personality traits CNN Image processing Handwritten text 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • José E. Valdez-Rodríguez
    • 1
  • Hiram Calvo
    • 1
  • Edgardo M. Felipe-Riverón
    • 1
  1. 1.Centro de Investigación en Computación, Instituto Politécnico NacionalCiudad de MéxicoMexico

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