You’ll need to install OpenCV, numpy and PIL If you want to try using this procedure to crop your own images, you can find Depending on how youĬount, I’d estimate that it gets a perfect crop on about 98% of the images, and This procedure worked well for my particular application. On what’s important, rather than the noise. The number of pixels, with no loss of text! This will help any OCR tool focus We do this repeatedly until there are only a few connected To do this, we apply binaryĭilation to the de-bordered edge image. Problem by finding individual chunks of text. The saving grace is that most crops don’t make much sense. Where W and H are the width and height of the image. The set of all possible crops is quite large: W 2 H 2, Score, the harmonic mean of precision and recall.
The precision is the fraction of the image outside the cropping rectangle.Ī fairly standard way to solve precision/recall problems is to optimize the F1.The recall is the fraction of white pixels inside the cropping rectangle.This should sound familiar: it’s a classic But we’d completely fail on goal #2: the crop These two goals are in opposition to one another. maximizes the number of white pixels inside it and.To smudges or marks on the original page.Īt this point, we’re looking for a crop (x1, y1, x2, y2) which: What we’re left with is an image with the text and possibly some other bits due
With polygons for the borders, it’s easy to black out everything outside them. Noticed that it performed much better on the Milstein images when I manuallyĬropped them down to just the text regions first: Source project developed over the past 20+ years at HP and Google. The most famous OCR program is Tesseract, a remarkably long-lived open Page layout analysis, a much less glamorous problem, is at least as important But it’s a dirty secret of the trade that When you hear “OCR”, you might think about fancy Machine Learning OCR programs typically have to do some sort of page-layout analysis toįind out where the text is and carve it up into individual lines andĬharacters.
The corresponding code with the Python SDK will be image_url = imagekit. Similarly, if we want to get a 400 x 300px resized image from ImageKit, the URL will contain height and width transformation parameters. Print(f"Original size : ) Example of generating a URL at width 200px with the Python SDK Pillow provides the resize() method, which takes a (width, height) tuple as an argument. Install the latest version of Pillow with pip. Pillow is one of the most popular options for performing basic image manipulation tasks such as cropping, resizing, or adding watermarks. We will be using an image by Asad from Pexels for all examples in this article.
The free plan has access to all the features we need for image resizing and other transformations. When we get to ImageKit later in this article, you will need to sign up for a free account on ImageKit's website. Make sure you have a recent version of Python installed on your system, preferably Python 3.6+, then spin up a virtual environment. Simplify all of it by using ImageKit, a complete image optimization product.This article will walk you through those options and look at ImageKit - a cloud-based, ready-to-use solution that offers real-time image manipulation. Python offers a rich set of options to perform some of the routine image resizing tasks. Resizing images is an integral part of the web, whether to display images on your website or app, store lower-resolution images, or generate a training set for neural networks.