| Título |
Handwritten Texts for Personality Identification Using Convolutional Neural Networks |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
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 |
| Resumen |
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. |
| Observaciones |
DOI 10.1007/978-3-030-05792-3_13
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Lugar |
Beijing |
| País |
China |
| No. de páginas |
140-145 |
| Vol. / Cap. |
11188 LNCS |
| Inicio |
2018-08-20 |
| Fin |
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| ISBN/ISSN |
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