Efficient Example-Based Super-Resolution of Single Text Images Based on Selective Patch Processing

2014 ◽  
Author(s):  
Nibal Nayef ◽  
Joseph Chazalon ◽  
Petra Gomez-Kramer ◽  
Jean-Marc Ogier
Keyword(s):  
Super Resolution ◽  
Single Text ◽  
Text Images

scholarly journals Resolution Enhancement for Low-resolution Text Images Using Generative Adversarial Network

2018 ◽  
Vol 246 ◽  
pp. 03040
Author(s):  
Jie Kong ◽  
Congying Wang
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Rejection Rate ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Optical Character ◽  
Text Images

In recent years, although Optical Character Recognition (OCR) has made considerable progress, low-resolution text images commonly appearing in many scenarios may still cause errors in recognition. For this problem, the technique of Generative Adversarial Network in super-resolution processing is applied to enhance the resolution of low-quality text images in this study. The principle and the implementation in TensorFlow of this technique are introduced. On this basis, a system is proposed to perform the resolution enhancement and OCR for low-resolution text images. The experimental results indicate that this technique could significantly improve the accuracy, reduce the error rate and false rejection rate of low-resolution text images identification.

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