Publications

@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}

}