title: How to split noise and text from the image for preprocessing of OCR
body: <p>I am applying OCR against subtitle in TV footage. (I am using Tesseact 3.x w/ C++) I am trying to split text and background part as a preprocessing of OCR.</p>
  
  <p>Here's the original image:</p>
  
  <p><a href="https://i.stack.imgur.com/sv9Pm.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/sv9Pm.png" alt="enter image description here"></a></p>
  
  <p>And, preprocessed image:</p>
  
  <p><a href="https://i.stack.imgur.com/aYoKa.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/aYoKa.png" alt="enter image description here"></a></p>
  
  <p>The OCR result is: Sicemn clone</p>
  
  <p>As the above preprocessed image shown, there're some "fog" remained around the letter which prevents OCR module to do their job properly.</p>
  
  <p>Is there any way to recognize those "fog" programatically to remove, or do some image processing to remove/reduce it from the preprocessed image?</p>
  
  <p>Since preprocessed logic is heavily optimized to handle different images, I rather want to find a way to "clean" the preprocessed image, than modifying preprocessed logic (since optimizing to this pics can affecting to other pics)</p>
  
  <p>Any suggestion is very welcome.</p>
  
  <hr>
  
  <p><strong>Update</strong></p>
  
  <p>Apparently, sixela's answer is great, and will work with most of the case.
  The case it does not work is background also include similar color of text</p>
  
  <p>Example of not working case:</p>
  
  <p><a href="https://i.stack.imgur.com/mzjp8.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/mzjp8.png" alt="enter image description here"></a></p>
  
  <p>Example of result:</p>
  
  <p><a href="https://i.stack.imgur.com/TDyPf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TDyPf.png" alt="enter image description here"></a></p>
  
  <p>Seemingly, Gaussian filter seems to cause a problem in this types of footage.
  This implies, different footage may requires different approach.</p>