<p>A) One thing you could try is get data for clear text images, train a GAN or some similar network by adding artificial noise to your images and passing that as input to train it for denoising, pass the image through that network and then pass it to a text detector/ocr engine (such as pytesseract or google vision ocr)</p>

<p>B) Train an image detector on your possible character set (something like YOLO or FasterRCNN) with added noise, you can do this by again, artificially adding noise to data, but might need some manual annotation.</p>

<p>C) You can try something like <a href="https://arxiv.org/abs/1803.09597" rel="nofollow noreferrer">this</a>, by checking the image for all alphabets/known characters and then combine the results. I personally would prefer this one.</p>

<p>PS. I haven't completely read the paper linked in C, but the images you linked seem to be closer to be solved by the one shot segmentation method rather than training a GAN.</p>

<p>PPS.Based on your comment under your question, make sure creating a captcha solving bot does not violate any legal conditions for using the site (I feel obligated to say this for some reason.)</p>
Image noise matches image text
<p>So I've been reading up on machine learning using TensorFlow and Keras, I've been trying to setup a dataset using some custom images and trying to learn the script to recognize the text while filtering out the noise, but the issue is that the noise color is the same and the text color which results in filtering out everything.</p>

<p>I'm not asking to get spoonfed, I just simply want pointers to the best way to solve/train the script to solve the text on the images.</p>

<p>What I'm looking for is to get the script to read on screen and calculate the word hidden in the image and print the result in the command line.</p>

<p><strong><em>There is no sample code since everything before was a failure and not actually what I was looking for.</em></strong></p>

<p><a href="https://imgur.com/a/3FZXHsg" rel="nofollow noreferrer">Album link for Imgur</a></p>