Today’s blog post is a continuation of our recent series on Optical Character Recognition (OCR) and computer vision.
In a previous blog post, we learned how to install the Tesseract binary and use it for OCR. We then learned how to cleanup images using basic image processing techniques to improve the output of Tesseract OCR.
However, as I’ve mentioned multiple times in these previous posts, Tesseract should not be considered a general, off-the-shelf solution for Optical Character Recognition capable of obtaining high accuracy.
In some cases, it will work great — and in others, it will fail miserably.
A great example of such a use case is credit card recognition, where given an input image,
we wish to:
- Detect the location of the credit card in the image.
- Localize the four groupings of four digits, pertaining to the sixteen digits on the credit card.
- Apply OCR to recognize the sixteen digits on the credit card.
- Recognize the type of credit card (i.e., Visa, MasterCard, American Express, etc.).
In these cases, the Tesseract library is unable to correctly identify the digits (this is likely due to Tesseract not being trained on credit card example fonts). Therefore, we need to devise our own custom solution to OCR credit cards.
In today’s blog post I’ll be demonstrating how we can use template matching as a form of OCR to help us create a solution to automatically recognize credit cards and extract the associated credit card digits from images.
To learn more about using template matching for OCR with OpenCV and Python, just keep reading.
Over the past few months I’ve gotten quite the number of requests landing in my inbox to build a bubble sheet/Scantron-like test reader using computer vision and image processing techniques.
And while I’ve been having a lot of fun doing this series on machine learning and deep learning, I’d be lying if I said this little mini-project wasn’t a short, welcome break. One of my favorite parts of running the PyImageSearch blog is demonstrating how to build actualsolutions to problems using computer vision.
In fact, what makes this project so special is that we are going to combine the techniques from many previous blog posts, including building a document scanner, contour sorting, andperspective transforms. Using the knowledge gained from these previous posts, we’ll be able to make quick work of this bubble sheet scanner and test grader.
You see, last Friday afternoon I quickly Photoshopped an example bubble test paper, printed out a few copies, and then set to work on coding up the actual implementation.
Overall, I am quite pleased with this implementation and I think you’ll absolutely be able to use this bubble sheet grader/OMR system as a starting point for your own projects.
To learn more about utilizing computer vision, image processing, and OpenCV to automatically grade bubble test sheets, keep reading.
Bubble sheet multiple choice scanner and test grader using OMR, Python and OpenCV
At the Recurse Center, I spent some time teaching myself image processing. When I started, I had no idea what it entailed. I just knew that it could help me recognize text, shapes and patterns and to do interesting things with them.
My sources have mainly been Wikipedia pages, books and publicly available university lecture notes. As I became more familiar with the material, I wished for an ‘Image Processing 101’ article that could give anyone a gentle introduction to the world of image processing.
This is my attempt at writing that article.
“In this post I’ll describe how I wrote a short (200 line) Python script to automatically replace facial features on an image of a face, with the facial features from a second image of a face.
The process breaks down into four steps:
- Detecting facial landmarks.
- Rotating, scaling, and translating the second image to fit over the first.
- Adjusting the colour balance in the second image to match that of the first.
- Blending features from the second image on top of the first.
The full source-code for the script can be found here…”
“So if you’re interested in building awesome computer vision based projects like this, then follow along with me and we’ll have OpenCV 3.0 with Python bindings installed on your Raspberry Pi 2 in no time…”
Installing OpenCV 3.0 for both Python 2.7 and Python 3+ on your Raspberry Pi 2