Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Review of Scene Text Detection and Recognition
https://orcid.org/0000-0002-1505-7857
Abstract Scene texts contain rich semantic information which may be used in many vision-based applications, and consequently detecting and recognizing scene texts have received increasing attention in recent years. In this paper, we first introduce the history and progress of scene text detection and recognition, and classify conventional methods in detail and point out their advantages as well as disadvantages. After that, we study these methods and illustrate the corresponding key issues and techniques, including loss function, multi-orientation, language model and sequence labeling. Finally, we describe commonly used benchmark datasets and evaluation protocols, based on which the performance of representative scene text detection and recognition methods are analyzed and compared.
Review of Scene Text Detection and Recognition
https://orcid.org/0000-0002-1505-7857
Abstract Scene texts contain rich semantic information which may be used in many vision-based applications, and consequently detecting and recognizing scene texts have received increasing attention in recent years. In this paper, we first introduce the history and progress of scene text detection and recognition, and classify conventional methods in detail and point out their advantages as well as disadvantages. After that, we study these methods and illustrate the corresponding key issues and techniques, including loss function, multi-orientation, language model and sequence labeling. Finally, we describe commonly used benchmark datasets and evaluation protocols, based on which the performance of representative scene text detection and recognition methods are analyzed and compared.
Review of Scene Text Detection and Recognition
https://orcid.org/0000-0002-1505-7857
Abstract Scene texts contain rich semantic information which may be used in many vision-based applications, and consequently detecting and recognizing scene texts have received increasing attention in recent years. In this paper, we first introduce the history and progress of scene text detection and recognition, and classify conventional methods in detail and point out their advantages as well as disadvantages. After that, we study these methods and illustrate the corresponding key issues and techniques, including loss function, multi-orientation, language model and sequence labeling. Finally, we describe commonly used benchmark datasets and evaluation protocols, based on which the performance of representative scene text detection and recognition methods are analyzed and compared.
Review of Scene Text Detection and Recognition
Lin, Han (Autor:in) / Yang, Peng (Autor:in) / Zhang, Fanlong (Autor:in)
2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Scene Text Detection and Recognition Using Maximally Stable Extremal Region
| DOAJ | 2024
Online Contents | 2000
Unsupervised Feature Learning for Land-Use Scene Recognition
| Online Contents | 2017
Unsupervised Feature Learning for Land-Use Scene Recognition
| Online Contents | 2017