…but will perform quite poorly if there is a significant amount of noise or your image is not properly preprocessed and cleaned before applying Tesseract. If you’ve read my previous post on Using Tesseract OCR with Python, you know that Tesseract can work very well under controlled conditions… Google adopted the project in 2006 and has been sponsoring it ever since. Tesseract, a highly popular OCR engine, was originally developed by Hewlett Packard in the 1980s and was then open-sourced in 2005. Using Tesseract with OpenCV’s EAST detector makes for a great combination. As of 2018, it now includes built-in deep learning capability making it a robust OCR tool (just keep in mind that no OCR system is perfect). Let’s go ahead and get started with OpenCV OCR! How to install Tesseract 4 Figure 1: The Tesseract OCR engine has been around since the 1980s. Once we have detected the text regions with OpenCV, we’ll then extract each of the text ROIs and pass them into Tesseract, enabling us to build an entire OpenCV OCR pipeline!įinally, I’ll wrap up today’s tutorial by showing you some sample results of applying text recognition with OpenCV, as well as discussing some of the limitations and drawbacks of the method.Performs text detection using OpenCV’s EAST text detector, a highly accurate deep learning text detector used to detect text in natural scene images.In order to perform OpenCV OCR text recognition, we’ll first need to install Tesseract v4 which includes a highly accurate deep learning-based model for text recognition.įrom there, I’ll show you how to write a Python script that: Looking for the source code to this post? Jump Right To The Downloads Section OpenCV OCR and text recognition with Tesseract
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