![]() Img = cv2.normalize(img, norm_img, 0, 255, cv2.NORM_MINMAX) Import cv2norm_img = np.zeros((img.shape, img.shape)) In this experiment, I’m using normalization, thresholding and image blur. Next we’ll try to use a little image processing to eliminate noise in the image. This means that tesseract cannot read words in images that have noise. No result after trying to pull text from an image with noise. However, in the real world it is difficult to find images that are really simple, so I will add noise to test the performance of the Tesseract. The results obtained from the Tesseract are good enough for simple images. filename = 'image_01.png'Īnd this is the result. Let’s load this image and convert it to text. A sample image for Tesseract to convert into text. I will use a simple image to test the usage of the Tesseract. More on Python: 5 Ways to Write More Pythonic CodeĪfter installation is completed, let’s move forward by applying Tesseract with Python. Let’s begin by getting pyTesseract installed. brew install Tesseractįor Windows, please see the Tesseract documentation. If you’re using Ubuntu, you can simply use apt-get to install Tesseract OCR: sudo apt-get install Tesseract-ocrįor macOS users, we’ll be using Homebrew to install Tesseract. In order to use the Tesseract library, we need to install it on our system. The first step is to install the Tesseract. In this article, we will start with the Tesseract OCR installation process, and test the extraction of text in images. It supports Unicode (UTF-8) and more than 100 languages. ![]() Tesseract runs on Windows, macOS and Linux platforms. Tesseract is an optical character recognition engine for various operating systems. One of the most common OCR tools that are used is the Tesseract. We can do this in Python using a few lines of code. It can be completed using the open-source OCR engine Tesseract. Python OCR is a technology that recognizes and pulls out text in images like scanned documents and photos using Python.
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