Publications
2022
1.
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.