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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Decisions about OpenCV and Tesseract OCR
Vladyslav Holubiev
Sr. Directory of Technology at Shelf · | 1 upvote · 49.8K views

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.

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Pros of OpenCV
Pros of Tesseract OCR
  • 37
    Computer Vision
  • 18
    Open Source
  • 12
    Imaging
  • 10
    Face Detection
  • 10
    Machine Learning
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Helping almost CV problem
  • 2
    Image Augmentation
  • 5
    Building training set is easy
  • 2
    Very lightweight library

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Cons of OpenCV
Cons of Tesseract OCR
    Be the first to leave a con
    • 1
      Works best with white background and black text

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    - No public GitHub repository available -

    What is OpenCV?

    OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

    What is Tesseract OCR?

    Tesseract was originally developed at Hewlett-Packard Laboratories Bristol and at Hewlett-Packard Co, Greeley Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some C++izing in 1998. In 2005 Tesseract was open sourced by HP. Since 2006 it is developed by Google.

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    What companies use OpenCV?
    What companies use Tesseract OCR?
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    What tools integrate with OpenCV?
    What tools integrate with Tesseract OCR?
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      What are some alternatives to OpenCV and Tesseract OCR?
      TensorFlow
      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
      CImg
      It mainly consists in a (big) single header file CImg.h providing a set of C++ classes and functions that can be used in your own sources, to load/save, manage/process and display generic images.
      OpenGL
      It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering.
      PyTorch
      PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
      OpenCL
      It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing.
      See all alternatives