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If you already have Tesseract installed on your Mac (if you followed my previous Tesseract install tutorial, for example), you’ll first want to unlink the original install: $ brew unlink tesseractĪnd from there you can run the install command. If you would like the latest Tesseract (as of this writing it is 5.0.0-alpha), then be sure to append the -HEAD switch at the end of the command. Currently, we recommend sticking with Tesseract 4. Update: Tesseract 5 (alpha release) is available. Just run the following command and Tesseract v4 will be installed on your Mac: $ brew install tesseract Installing Tesseract on macOS is straightforward provided you have Homebrew, macOS’ “unofficial” package manager, installed on your system.
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Just add the alex-p/tesseract-ocr PPA repository to your system, update your package definitions, and then install Tesseract: $ sudo add-apt-repository ppa:alex-p/tesseract-ocrĪssuming there are no errors, you should now have Tesseract 4 installed on your machine. The good news is that Alexander Pozdnyakov has created an Ubuntu PPA (Personal Package Archive) for Tesseract, which makes it super easy to install Tesseract 4 on older versions of Ubuntu. If you’re using Ubuntu 14, 16, or 17 though, you’ll need a few extra commands due to dependency requirements. To check your Ubuntu version you can use the lsb_release command: $ lsb_release -aĪs you can see, I am running Ubuntu 18.04 but you should check your Ubuntu version before continuing.įor Ubuntu 18.04 users, Tesseract 4 is part of the main apt-get repository, making it super easy to install Tesseract via the following command: $ sudo apt install tesseract-ocr
The exact commands used to install Tesseract 4 on Ubuntu will be different depending on whether you are using Ubuntu 18.04 or Ubuntu 17.04 and earlier.
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To install OpenCV on your system, just follow one of my OpenCV installation guides, ensuring that you download the correct/desired version of OpenCV and OpenCV-contrib in the process.
To run today’s script you’ll need OpenCV installed. Let’s get started configuring your machine! Install OpenCV
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Later in this blog post, you’ll learn how to combine OpenCV’s EAST text detection algorithm with Tesseract v4 in a single Python script to automatically perform OpenCV OCR. In the remainder of this section, you will learn how to install Tesseract v4 on your machine. The underlying OCR engine itself utilizes a Long Short-Term Memory (LSTM) network, a kind of Recurrent Neural Network (RNN). The latest release of Tesseract (v4) supports deep learning-based OCR that is significantly more accurate. It was only a matter of time until Tesseract incorporated a deep learning model to further boost OCR accuracy - and in fact, that time has come. Just as deep learning has impacted nearly every facet of computer vision, the same is true for character recognition and handwriting recognition.ĭeep learning-based models have managed to obtain unprecedented text recognition accuracy, far beyond traditional feature extraction and machine learning approaches. …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:
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Looking for the source code to this post? Jump Right To The Downloads Section OpenCV OCR and text recognition with Tesseract