Publications
2020
1.
Blanco-Medina, Pablo; Fidalgo, Eduardo; Alegre, Enrique; Alaiz-Rodríguez, Rocío; Jáñez-Martino, Francisco; Bonnici, Alexandra
Rectification and super-resolution enhancements for forensic text recognition Artículo de revista
En: Sensors, vol. 20, no 20, pp. 5850, 2020, (Publisher: MDPI).
Resumen | Enlaces | BibTeX | Etiquetas: Compruter forensics, Super-Resolution, Text Recognition, text spotting, Tor Darknet
@article{blanco-medina_rectification_2020,
title = {Rectification and super-resolution enhancements for forensic text recognition},
author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Rocío Alaiz-Rodríguez and Francisco Jáñez-Martino and Alexandra Bonnici},
url = {https://www.mdpi.com/1424-8220/20/20/5850},
year = {2020},
date = {2020-01-01},
journal = {Sensors},
volume = {20},
number = {20},
pages = {5850},
abstract = {This paper focuses on improving text extraction from images, a challenge often encountered in environments like the Tor Darknet and Child Sexual Abuse (CSA) content, where accurate text retrieval is essential for identifying illegal activities. The authors evaluate eight text recognizers and enhance performance by integrating rectification networks and super-resolution algorithms. Testing on multiple datasets (TOICO-1K and CSA-text) showed improvements, with the highest performance increase on the ICDAR 2015 dataset. The combination of rectification and super-resolution yielded the best results, particularly when using deep learning models like CNNs.},
note = {Publisher: MDPI},
keywords = {Compruter forensics, Super-Resolution, Text Recognition, text spotting, Tor Darknet},
pubstate = {published},
tppubtype = {article}
}
This paper focuses on improving text extraction from images, a challenge often encountered in environments like the Tor Darknet and Child Sexual Abuse (CSA) content, where accurate text retrieval is essential for identifying illegal activities. The authors evaluate eight text recognizers and enhance performance by integrating rectification networks and super-resolution algorithms. Testing on multiple datasets (TOICO-1K and CSA-text) showed improvements, with the highest performance increase on the ICDAR 2015 dataset. The combination of rectification and super-resolution yielded the best results, particularly when using deep learning models like CNNs.
2019
2.
Blanco-Medina, Pablo; Fidalgo, Eduardo; Alegre, Enrique; Jánez-Martino, Francisco
Improving text recognition in Tor darknet with rectification and super-resolution techniques Artículo de revista
En: 2019, (Publisher: IET Digital Library).
Resumen | Enlaces | BibTeX | Etiquetas: Super-Resolution, Text Recognition, text spotting, Tor Darknet
@article{blanco-medina_improving_2019,
title = {Improving text recognition in Tor darknet with rectification and super-resolution techniques},
author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Francisco Jánez-Martino},
url = {https://ieeexplore.ieee.org/abstract/document/9136610},
year = {2019},
date = {2019-01-01},
abstract = {This paper investigates combining super-resolution algorithms with a rectification network to improve text recognition in low-resolution images, particularly in the Tor darknet. The results show that combining these methods yields the best performance, with improvements of 3.77% on the ICDAR 2015 dataset and 3.41% on the TOICO-1K Tor dataset. Rectification alone outperforms super-resolution, but the combination provides the best results.},
note = {Publisher: IET Digital Library},
keywords = {Super-Resolution, Text Recognition, text spotting, Tor Darknet},
pubstate = {published},
tppubtype = {article}
}
This paper investigates combining super-resolution algorithms with a rectification network to improve text recognition in low-resolution images, particularly in the Tor darknet. The results show that combining these methods yields the best performance, with improvements of 3.77% on the ICDAR 2015 dataset and 3.41% on the TOICO-1K Tor dataset. Rectification alone outperforms super-resolution, but the combination provides the best results.