Hi,

This week you'll learn about OCR'ing Video Streams.

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As you know by now, Deep Learning has had a profound impact in the world of image processing and, in turn, in video processing. Today's computer systems are fast enough to decompose video clips into individual frames and process them accordingly. Hence, most topics that we learn about image processing can be used in video processing. 

Needless to say, video tasks in computer vision (CV) have grown at an exponential rate, to the point where many important tasks you see in your daily life are accomplished just by this. But this also means these tasks depend on flawless uninterrupted video streams, which is seldom possible in the real world.

Hence, today's tutorial, OCR'ing Video Streams, deals with a variety of methods you can plug into your video processing task to deal with scenarios where some external factor might mess up the optical character recognition in video frames. 

The big picture: Considering how important video streams are to today's communication world, the failsafe methods learned in this tutorial will provide you with one of the ways you can avoid data misinterpretation. 

How it works: We have used a series of transformations, starting with Fast Fourier Transform, filtering out images that are too noisy to process. We then use contour approximation and perspective transformation techniques to zero out the exact region of text we require.

Our thoughts: It's always handy to keep a set of image processing techniques at your disposal. Real-world scenarios have innumerable possible factors which aren't in your control. Hence it's best to stay prepared.

Yes, but: The techniques learned in this tutorial do not cover all possible scenarios when dealing with video streams. 

Stay smart: Do not stop here. There always is something more to learn in the world of Computer Vision. 

Click here to read the full tutorial

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