![]() Step 2: Load Images and Extract Text using EasyOCRįor copyright reasons, all images used in the sample notebook are not provided in the GitHub repo. You’re off to a great start! Now, onto the next step. It sets the backend of the Matplotlib module to display figures inline and not on a separate window. Note that the %matplotlib inline magic command is exclusive for Jupyter notebooks. At this point, you should be able to execute the following lines of code in your notebook: import cv2 Now, go ahead and install the EasyOCR module - the tool we need for extracting text from images. You can follow the tutorial in our interactive Jupyter notebook online:įigure 1: Customized PyTorch installation command. For this tutorial, we will need OpenCV, Matplotlib, Numpy, PyTorch, and EasyOCR modules. An easy task for humans, but more work for computers to identify text from image pixels. Optical character recognition is a process of reading text from images. Step 1: Install and Import Required Modules Third, we will use OpenCV to overlay detected texts on the original images.Second, we will perform image-to-text processing using EasyOCR on various images.First, we will install the required libraries.In this article, we will go through a three-step tutorial. It is a new, deep learning-based module for reading text from all kinds of images in more than 80 languages. You may ask: Is there any alternative that is as good as PyTesseract for OCR? Yes, EasyOCR it is. ![]() It uses classical computer vision methods to perform optical character recognition (OCR), then features neural network components such as LSTM from its fourth version onwards. ![]() We covered the syntax of the cv2.putText() function along with various examples for a better understanding of beginners.First released in 2007, PyTesseract is the to-go library for extracting text from images. Hope you found this quick tutorial on writing text on images in OpenCV quite helpful.
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