Publications
2024
1.
Blanco-Medina, Pablo; Biswas, Rubel; González-Castro, Victor; Alaiz-Rodríguez, Rocío; Fidalgo, Eduardo; Alegre, Enrique
Mejoras en extracción de URLs en smishing mediante text spotting Artículo de revista
En: Jornadas de Automática, no 45, 2024.
Resumen | Enlaces | BibTeX | Etiquetas: CERT, OCR, Smishing, text spotting
@article{blanco-medina_mejoras_2024,
title = {Mejoras en extracción de URLs en smishing mediante text spotting},
author = {Pablo Blanco-Medina and Rubel Biswas and Victor González-Castro and Rocío Alaiz-Rodríguez and Eduardo Fidalgo and Enrique Alegre},
url = {https://revistas.udc.es/index.php/JA_CEA/article/view/10954},
year = {2024},
date = {2024-01-01},
journal = {Jornadas de Automática},
number = {45},
abstract = {Este trabajo propone un proceso para extraer URLs de Smishing de capturas de pantalla, utilizando técnicas de Text Spotting y una reconstrucción personalizada de URLs. Al aplicar esta metodología en 244 capturas y 262 URLs, se mejora la precisión del reconocimiento de un 3,05% a un 22,90%, optimizando el procesamiento de texto en Smishing.},
keywords = {CERT, OCR, Smishing, text spotting},
pubstate = {published},
tppubtype = {article}
}
Este trabajo propone un proceso para extraer URLs de Smishing de capturas de pantalla, utilizando técnicas de Text Spotting y una reconstrucción personalizada de URLs. Al aplicar esta metodología en 244 capturas y 262 URLs, se mejora la precisión del reconocimiento de un 3,05% a un 22,90%, optimizando el procesamiento de texto en Smishing.
2022
2.
Blanco-Medina, Pablo; Fidalgo, Eduardo; Alegre, Enrique; González-Castro, Víctor
A survey on methods, datasets and implementations for scene text spotting Artículo de revista
En: IET Image Processing, vol. 16, no 13, pp. 3426–3445, 2022.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, image text detection, OCR, text spotting
@article{blanco-medina_survey_2022,
title = {A survey on methods, datasets and implementations for scene text spotting},
author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Víctor González-Castro},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ipr2.12574},
year = {2022},
date = {2022-01-01},
journal = {IET Image Processing},
volume = {16},
number = {13},
pages = {3426–3445},
abstract = {ext Spotting combines the tasks of detecting and transcribing text present in images, addressing challenges like orientation, aspect ratio, vertical text, and multiple languages in a single image. This paper analyzes and compares the most recent methods and publications in the field, extending beyond traditional comparisons of architectures and performance. It also discusses aspects often overlooked, such as hardware, software, backbone architectures, main challenges, and programming languages used in algorithms. The review covers research from 2016 to 2022, highlighting current problems, future trends, and providing a baseline for the development and comparison of future Text Spotting methods.},
keywords = {Computer vision, image text detection, OCR, text spotting},
pubstate = {published},
tppubtype = {article}
}
ext Spotting combines the tasks of detecting and transcribing text present in images, addressing challenges like orientation, aspect ratio, vertical text, and multiple languages in a single image. This paper analyzes and compares the most recent methods and publications in the field, extending beyond traditional comparisons of architectures and performance. It also discusses aspects often overlooked, such as hardware, software, backbone architectures, main challenges, and programming languages used in algorithms. The review covers research from 2016 to 2022, highlighting current problems, future trends, and providing a baseline for the development and comparison of future Text Spotting methods.
2020
3.
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
4.
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.