Local representations for forward scattering

1972 ◽  
Vol 31 (1-3) ◽  
pp. 273-276
Author(s):  
F. Niedermayer
2004 ◽  
Vol 69 (4) ◽  
Author(s):  
S. G. Romanov ◽  
C. M. Sotomayor Torres

2021 ◽  
Vol 2 (2) ◽  
pp. 1-18
Author(s):  
Hongchao Gao ◽  
Yujia Li ◽  
Jiao Dai ◽  
Xi Wang ◽  
Jizhong Han ◽  
...  

Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most networks capture the sequence only from a single-granularity visual representation, which to some extent limits the performance of recognition. In this article, we propose a hierarchical attention network to capture multi-granularity deep local representations for recognizing irregular scene text. It consists of several hierarchical attention blocks, and each block contains a Local Visual Representation Module (LVRM) and a Decoder Module (DM). Based on the hierarchical attention network, we propose a scene text recognition network. The extensive experiments show that our proposed network achieves the state-of-the-art performance on several benchmark datasets including IIIT-5K, SVT, CUTE, SVT-Perspective, and ICDAR datasets under shorter training time.


2007 ◽  
Vol 22 (8) ◽  
pp. 1664-1671 ◽  
Author(s):  
Padmapriya P. Banada ◽  
Songling Guo ◽  
Bulent Bayraktar ◽  
Euiwon Bae ◽  
Bartek Rajwa ◽  
...  

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