Feb 28, 2012 · I've noticed that text that is highly pixellated - for example that generated by fax machines - is especially difficult for tesseract to process - presumably all those jagged edges to the characters confound the shape-recognition algorithms. What sort of image processing techniques would improve the accuracy?

Jan 10, 2012 · Here are a list of library that you'll want to explore for doing efficient image processing: OpenCV - is a library of programming functions for real time computer vision and image manipulation that contains Python bindings.

Mar 4, 2014 · I'm working on teaching myself the basics of computerized image processing, and I am teaching myself Python at the same time. Given an image x of dimensions 2048x1354 with 3 channels, efficiently calculate the histogram of the pixel intensities.

Feb 4, 2016 · I am trying to implement the Wiener Filter to perform deconvolution on blurred image. My implementation is like this import numpy as np from numpy.fft import fft2, ifft2 def wiener_filter(img, ker...

Sep 12, 2019 · But new OpenCV-Python interface has full support to Numpy. Numpy is a package for scientific computing in Python. It is also a wrapper around native C code. It is a highly optimized library which supports a wide variety of matrix operations, highly suitable for …

May 17, 2019 · Input: 2 images with some differences. Expected Output: 3 images: the two input images but with the differences highlighted (clearly highlighted in a configurable color), and a third image containing only the differences (the mask).

Apr 25, 2012 · I have a problem with filling white holes inside a black coin so that I can have only 0-255 binary images with filled black coins. I have used a Median filter to accomplish it but in that case conn...

Jun 26, 2017 · I'd like to remove shadow before image binarization using OpenCV. I've tried Otsu Method and adaptive thresholding, however for images where there are large regions of shadow, these two methods wil...

Mar 15, 2013 · img = cv2.imread('image.png') img = img[c1:c1+25,r1:r1+25] Here c1 is the left side column pixel location, and r1 is the corresponding row location. And img now has the image specified within the pixels as the ROI. EDIT: Very nicely explained here, How to copy a image region using opencv in python?

I'm trying to stream FLIR Lepton 3.5 with Python OPENCV using "VideoCapture" & "cv2.imshow" script. Then, I'll do some detection and control. Here's the problem I have, I was only able to get a very faint black/gray video stream with what seems to be a couple of rows of dead pixels at the bottom of the stream.