An End to End Method for Scene Text Detection and Recognition

2021 ◽  
pp. 542-547
Author(s):  
Jing Geng ◽  
Congjun Cao
Keyword(s):  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text ◽  
End To End ◽  
Detection And Recognition

EEM: An End-to-end Evaluation Metric for Scene Text Detection and Recognition

2021 ◽  
pp. 95-108
Author(s):  
Jiedong Hao ◽  
Yafei Wen ◽  
Jie Deng ◽  
Jun Gan ◽  
Shuai Ren ◽  
...  
Keyword(s):  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text ◽  
End To End ◽  
Evaluation Metric ◽  
Detection And Recognition

Review of Scene Text Detection and Recognition

2019 ◽  
Vol 27 (2) ◽  
pp. 433-454 ◽  
Author(s):  
Han Lin ◽  
Peng Yang ◽  
Fanlong Zhang
Keyword(s):  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text ◽  
Detection And Recognition

scholarly journals All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting

2020 ◽  
Vol 34 (07) ◽  
pp. 12160-12167 ◽  
Author(s):  
Hao Wang ◽  
Pu Lu ◽  
Hui Zhang ◽  
Mingkun Yang ◽  
Xiang Bai ◽  
...  
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Text Detection ◽  
Text Recognition ◽  
Scene Text Detection ◽  
Scene Text ◽  
End To End ◽  
Text Spotting ◽  
Set Of Points ◽  
Instance Segmentation

Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Different from the existing approaches that formulate text detection as bounding box extraction or instance segmentation, we localize a set of points on the boundary of each text instance. With the representation of such boundary points, we establish a simple yet effective scheme for end-to-end text spotting, which can read the text of arbitrary shapes. Experiments on three challenging datasets, including ICDAR2015, TotalText and COCO-Text demonstrate that the proposed method consistently surpasses the state-of-the-art in both scene text detection and end-to-end text recognition tasks.

scholarly journals Deep learning for scene text detection and recognition

2018 ◽  
Vol 48 (5) ◽  
pp. 531-544 ◽  
Author(s):  
Xiang BAI ◽  
Minghui LIAO ◽  
Baoguang SHI ◽  
Mingkun YANG
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text ◽  
Detection And Recognition
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