RT info:eu-repo/semantics/article
T1 Rectification and Super-Resolution Enhancements for Forensic Text Recognition
A1 Blanco Medina, Pablo
A1 Fidalgo Fernández, Eduardo
A1 Alegre Gutiérrez, Enrique
A1 Alaiz Rodríguez, Rocío
A1 Jáñez Martino, Francisco
A1 Bonnici, Alexandra
A2 Ingenieria de Sistemas y Automatica
K1 Cibernética
K1 Informática
K1 Text spotting
K1 Text recognition
K1 Super-resolution
K1 Tor Darknet
K1 Computer forensics
K1 3304.05 Sistemas de Reconocimiento de Caracteres
K1 1207.03 Cibernética
K1 1203.17 Informática
AB [EN] Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.
PB MDPI
LK https://hdl.handle.net/10612/20387
UL https://hdl.handle.net/10612/20387
NO Blanco-Medina, P., Fidalgo, E., Alegre, E., Alaiz-Rodríguez, R., Jáñez-Martino, F., & Bonnici, A. (2020). Rectification and super-resolution enhancements for forensic text recognition. Sensors (Switzerland), 20(20), 1-17. https://doi.org/10.3390/S20205850
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 27-jun-2024