An Effective Approach Towards Video Text Recognition

2014 ◽  
pp. 323-333
Author(s):  
Prakash Sudir ◽  
M. Ravishankar
Keyword(s):  
Text Recognition

scholarly journals Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 18569-18584
Author(s):  
Najoua Rahal ◽  
Maroua Tounsi ◽  
Amir Hussain ◽  
Adel M. Alimi
Keyword(s):  
Text Recognition ◽  
Arabic Text

scholarly journals Offline Handwritten Text Recognition Using Deep Learning: A Review

2021 ◽  
Vol 1848 (1) ◽  
pp. 012015
Author(s):  
Yintong Wang ◽  
Wenjie Xiao ◽  
Shuo Li
Keyword(s):  
Deep Learning ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition

scholarly journals Multi-granularity Deep Local Representations for Irregular Scene Text Recognition

2021 ◽  
Vol 2 (2) ◽  
pp. 1-18
Author(s):  
Hongchao Gao ◽  
Yujia Li ◽  
Jiao Dai ◽  
Xi Wang ◽  
Jizhong Han ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Representation ◽  
Text Recognition ◽  
Natural Scene ◽  
Attention Network ◽  
Training Time ◽  
Scene Text ◽  
Benchmark Datasets ◽  
Local Representations ◽  
Scene Text Recognition

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.

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