Two weeks ago OpenCV 3.3 was officially released, bringing with it a highly improved deep learning ( dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. Furthermore, this API for using pre-trained deep learning models is compatible with both the C++ API and the Python bindings, making it […]
Long exposure with OpenCV and Python
One of my favorite photography techniques is long exposure, the process of creating a photo that shows the effect of passing time, something that traditional photography does not capture. When applying this technique, water becomes silky smooth, stars in a night sky leave light trails as the earth rotates, and car headlights/taillights illuminate highways in […]
Announcing PyImageJobs: A computer vision and deep learning jobs board
Today, I am pleased to announce that PyImageJobs has officially launched. Whether you are (1) looking to find a job in the computer vision, OpenCV, or deep learning space or (2) trying to fill a computer vision position for your company, organization, or project — I have you covered. Even though PyImageJobs just launched, the massive PyImageSearch network and community has already made […]
Bank check OCR with OpenCV and Python (Part II)
Today’s blog post is Part II in our two part series on OCR’ing bank check account and routing numbers using OpenCV, Python, and computer vision techniques. Last week we learned how to extract MICR E-13B digits and symbols from input images. Today we are going to take this knowledge and use it to actually recognize […]
Bank check OCR with OpenCV and Python (Part I)
Today’s blog post is inspired by Li Wei, a PyImageSearch reader who emailed me last week and asked: Hi Adrian, Thank you for the PyImageSearch blog. I read it each week and look forward to your new posts every Monday. I really enjoyed last week’s tutorial on credit card OCR. I was wondering: Can this […]
Credit card OCR with OpenCV and Python
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. […]
Using Tesseract OCR with Python
In last week’s blog post we learned how to install the Tesseract binary for Optical Character Recognition (OCR). We then applied the Tesseract program to test and evaluate the performance of the OCR engine on a very small set of example images. As our results demonstrated, Tesseract works best when there is a (very) clean […]
Installing Tesseract for OCR
Today’s blog post is part one in a two part series on installing and using the Tesseract library for Optical Character Recognition (OCR). OCR is the automatic process of converting typed, handwritten, or printed text to machine-encoded text that we can access and manipulate via a string variable. Part one of this series will focus […]
Labeling superpixel colorfulness with OpenCV and Python
After our previous post on computing image colorfulness was published, Stephan, a PyImageSearch reader, left a comment on the tutorial asking if there was a method to compute the colorfulness of specific regions of an image (rather than the entire image). There are multiple ways of attacking this problem. The first could be to apply […]
Image Difference with OpenCV and Python
In a previous PyImageSearch blog post, I detailed how to compare two images with Python using the Structural Similarity Index (SSIM). Using this method, we were able to easily determine if two images were identical or had differences due to slight image manipulations, compression artifacts, or purposeful tampering. Today we are going to extend the […]