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OpenCV vs Tesseract OCR: What are the differences?
Introduction
In this article, we will explore the key differences between OpenCV and Tesseract OCR.
Technology Focus: OpenCV is primarily a computer vision library that provides various algorithms and functions for image and video analysis. It focuses on tasks such as object detection, image processing, and feature extraction. On the other hand, Tesseract OCR is a specific optical character recognition (OCR) engine developed by Google. Its main purpose is to recognize and extract text from images.
Functionality: OpenCV offers a wide range of functionalities, including image and video processing, feature detection, image stitching, and augmented reality. It provides a comprehensive set of tools and algorithms for computer vision tasks. Tesseract OCR, on the other hand, is specifically designed for text recognition. It excels in accurately extracting text from images, even under various lighting and noise conditions.
Language Support: OpenCV supports multiple programming languages such as C++, Python, Java, and MATLAB, making it more accessible for developers with different language preferences. Tesseract, on the other hand, primarily supports the English language natively, but with additional training, it can also support other languages. The language support in Tesseract is more focused on the OCR functionality rather than providing extensive multi-language support.
Complexity and Learning Curve: OpenCV is a comprehensive library with a steep learning curve. It requires a good understanding of computer vision concepts and algorithms to fully utilize its capabilities. On the other hand, Tesseract OCR is relatively easier to learn and implement for performing OCR tasks. It provides a simple API for integrating OCR functionality into applications without needing in-depth knowledge of computer vision.
Accuracy and Performance: OpenCV is known for its high accuracy and performance in computer vision tasks. It provides various algorithms that can achieve state-of-the-art results in image and video processing. Tesseract OCR, on the other hand, focuses on accuracy in text recognition. It has been trained on a large dataset of text samples, resulting in high accuracy in extracting text from images.
Community and Support: OpenCV has a large and active community of developers, researchers, and enthusiasts. It has extensive documentation, tutorials, and online forums, making it easier to find answers and get support. Tesseract OCR also has a supportive community, but its focus is primarily on OCR-related discussions and support.
In Summary, OpenCV is a versatile computer vision library that provides a broad range of image and video processing functionalities, while Tesseract OCR is specifically designed for accurate text recognition from images.
AWS Rekognition has an OCR feature but can recognize only up to 50 words per image, which is a deal-breaker for us. (see my tweet).
Also, we discovered fantastic speed and quality improvements in the 4.x versions of Tesseract. Meanwhile, the quality of AWS Rekognition's OCR remains to be mediocre in comparison.
We run Tesseract serverlessly in AWS Lambda via aws-lambda-tesseract library that we made open-source.
Pros of OpenCV
- Computer Vision37
- Open Source18
- Imaging12
- Face Detection10
- Machine Learning10
- Great community6
- Realtime Image Processing4
- Helping almost CV problem2
- Image Augmentation2
Pros of Tesseract OCR
- Building training set is easy5
- Very lightweight library2
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Cons of OpenCV
Cons of Tesseract OCR
- Works best with white background and black text1