![]() Unlike linear filters, median blurring replaces the pixel values with the median value available in the neighborhood values. Particularly, this outperforms other blurring methods in removing salt-and-pepper noise in the images. The central element in the kernel area is replaced with the median of all the pixels under the kernel. On the contrary, gaussian blurring does not preserve the edges in the input. Gaussian blurring is very useful for removing - guess what? - gaussian noise from the image. ![]() Here, the dimensions of the kernel and standard deviations in both directions can be determined independently. This works in a similar fashion to Averaging, but it uses Gaussian kernel, instead of a normalized box filter, for convolution. AveragingĪfter convolving an image with a normalized box filter, this simply takes the average of all the pixels under the kernel area and replaces the central element. While filters are usually used to blur the image or to reduce noise, there are a few differences between them. Image blurring is usually achieved by convolving the image with a low-pass filter kernel. It’s worth mentioning that there are a few blur filters available in the OpenCV library. img = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR) Blurring If you’d like to trade off some of your image quality for faster performance, you may want to try INTER_LINEAR for enlarging images. img = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC) In this case, INTER_CUBIC generally performs better than other alternatives, though it’s also slower than others. On the other hand, as in most cases, you may need to scale your image to a larger size to recognize small characters. (Btw, the parameters fx and fy denote the scaling factor in the function below.) img = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) If you’re interested in shrinking your image, INTER_AREA is the way to go for you. The images that are rescaled are either shrunk or enlarged. Now, it’s time to get down to details and show you a few settings you can play with. But if you liked the first story, here comes the sequel! So where did we leave off?Īh, we had a brief overview of rescaling, noise removal, and binarization. In the previous story, I didn’t bother going into details for the most part. Now, I’m keen on showing you a few more tricks and stuff you can do with Tesseract and OpenCV to improve your overall accuracy. It is a pretty simple overview, but it should help you get started with Tesseract and clear some hurdles that I faced when I was in your shoes. Previously, on How to get started with Tesseract, I gave you a practical quick-start tutorial on Tesseract using Python. To have a good view of array in notebook result, I have resized the image to 18 X 18.By Berk Kaan Kuguoglu How to use image preprocessing to improve the accuracy of Tesseract The shape of this image can be found using the shape attribute as it is an array of pixels(again numbers). If we do not mention the cmap value, matplotlib will automatically assign a colormap to it. This image can be visualized using matplotlib.pyplot imshow(img). the image will only be a 2D array and the 3rd dimension for color will not be added. 0 as second parameter will read the image in gray-scale mode. #To visualize the image we can use matplotlib.pyplotįrom the above program, you can see that we have a image “4.png”.Ĭv2.imread(filename, 0) – Read and returns the image.Ġ mentions the color of the image. Lets consider we have a small image of 28 X 28 pixels. ![]() We all know that an image is nothing but an array of pixels.īased on the number the color of the pixel will be.Įxample: 0 – Black and 1 – White. Supported Versions: 3.6, 3.7, 3.8, 3.9 Basic understanding of image:
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