Here’s an idea. We break this problem up into several steps: Determine average rectangular contour area. We threshold then find contours and filter using the bounding rectangle area of the contour. The reason we do this is because of the observation that any typical character will only be so big whereas large noise will span … Read more
I have a solution that works, but you’ll have to translate it to OpenCV yourself. It’s written in Mathematica. The first step is to adjust the brightness in the image, by dividing each pixel with the result of a closing operation: src = ColorConvert[Import[“http://davemark.com/images/sudoku.jpg”], “Grayscale”]; white = Closing[src, DiskMatrix[5]]; srcAdjusted = Image[ImageData[src]/ImageData[white]] The next step … Read more
The specific numbers in the question are from CCIR 601 (see Wikipedia article). If you convert RGB -> grayscale with slightly different numbers / different methods, you won’t see much difference at all on a normal computer screen under normal lighting conditions — try it. Here are some more links on color in general: Wikipedia … Read more
Well, I decided to workout myself on my question to solve above problem. What I wanted is to implement a simpl OCR using KNearest or SVM features in OpenCV. And below is what I did and how. ( it is just for learning how to use KNearest for simple OCR purposes). 1) My first question … Read more
Here’s an implementation of the Projection Profile Method algorithm for skew angle estimation. Various angle points are projected into an accumulator array where the skew angle can be defined as the angle of projection within a search interval that maximizes alignment. The idea is to rotate the image at various angles and generate a histogram … Read more
Here is my solution: import pytesseract from PIL import Image, ImageEnhance, ImageFilter im = Image.open(“temp.jpg”) # the second one im = im.filter(ImageFilter.MedianFilter()) enhancer = ImageEnhance.Contrast(im) im = enhancer.enhance(2) im = im.convert(‘1’) im.save(‘temp2.jpg’) text = pytesseract.image_to_string(Image.open(‘temp2.jpg’)) print(text)
This is a recurring subject in Stackoverflow and since I was unable to find a relevant implementation I decided to accept the challenge. I made some modifications to the squares demo present in OpenCV and the resulting C++ code below is able to detect a sheet of paper in the image: void find_squares(Mat& image, vector<vector<Point> … Read more
Problem 1 : Different applications use different scales for HSV. For example gimp uses H = 0-360, S = 0-100 and V = 0-100. But OpenCV uses H: 0-179, S: 0-255, V: 0-255. Here i got a hue value of 22 in gimp. So I took half of it, 11, and defined range for that. … Read more
You’re most likely getting that error due to an invalid stream link. Insert your stream link into VLC player to confirm it is working. Here’s a IP camera video streaming widget using OpenCV and cv2.VideoCapture.read(). This implementation uses threading for obtaining frames in a different thread since read() is a blocking operation. By putting this … Read more
Below are three approaches to solving this problem (and there are many others). The first is a standard approach in computer vision, keypoint matching. This may require some background knowledge to implement, and can be slow. The second method uses only elementary image processing, and is potentially faster than the first approach, and is straightforward … Read more