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Título
Detecting Textual Information in Images from Onion Domains Using Text Spotting
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XXXIX Jornadas de Automática: actas. Badajoz, 5-7 de septiembre de 2018
Cita Bibliográfica
Blanco Medina, P., Fidalgo Fernández, E., Alegre Gutiérrez, E., & Wesam Al Nabki, M. (2018). Detecting Textual Information in Images from Onion Domains Using Text Spotting. En I. Tejado Balsera, E. Pérez Hernández, A. J. Calderón Godoy, I. González Pérez, P. Merchán García, J. S. Lozano Rogado, S. Salamanca Miño, & B. M. Vinagre Jara (eds.), XXXIX Jornadas de Automática: actas. Badajoz, 5-7 de septiembre de 2018. https://doi.org/10.17979/SPUDC.9788497497565.0975
Editorial
Universidad de Extremadura
Fecha
2018
Resumen
[EN] Due to the efforts of different authorities in the fight against illegal activities in the Tor networks, the traders have developed new ways of circumventing the monitoring tools used to obtain evidence of said activities. In particular, embedding textual content into graphical objects avoids that text analysis, using Natural Language Processing (NLP) algorithms, can be used for watching such onion web contents. In this paper, we present a Text Spotting framework dedicated to detecting and recognizing textual information within images hosted in onion domains. We found that the Connectionist Text Proposal Network and Convolutional Recurrent Neural Network achieve 0.57 F-Measure when running the combined pipeline on a subset of 100 images labeled manually obtained from TOIC dataset. We also identified the parameters that have a critical influence on the Text Spotting results. The proposed technique might support tools to help the authorities in detecting these activities.
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