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OpenCV vs Tesseract OCR

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OpenCV
1K
1K
+ 1
97
Tesseract OCR
86
259
+ 1
5
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OpenCV vs Tesseract OCR: What are the differences?

Developers describe OpenCV as "Open Source Computer Vision Library". 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. On the other hand, Tesseract OCR is detailed as "Tesseract Open Source OCR Engine". 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.

OpenCV belongs to "Image Processing and Management" category of the tech stack, while Tesseract OCR can be primarily classified under "Image Analysis API".

OpenCV and Tesseract OCR are both open source tools. It seems that OpenCV with 36.3K GitHub stars and 26.6K forks on GitHub has more adoption than Tesseract OCR with 28.1K GitHub stars and 5.38K GitHub forks.

Lensley, Plickers, and Suggestic are some of the popular companies that use OpenCV, whereas Tesseract OCR is used by Shelf, ESCHR, and DLabs. OpenCV has a broader approval, being mentioned in 39 company stacks & 39 developers stacks; compared to Tesseract OCR, which is listed in 6 company stacks and 6 developer stacks.

Decisions about OpenCV and Tesseract OCR
Vladyslav Holubiev
Sr. Directory of Technology at Shelf · | 1 upvote · 38.9K views
Chose
Tesseract OCRTesseract OCR
over
Amazon RekognitionAmazon Rekognition
at
()

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
  • 36
    Computer Vision
  • 17
    Open Source
  • 12
    Imaging
  • 9
    Face Detection
  • 9
    Machine Learning
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Image Augmentation
  • 2
    Helping almost CV problem
  • 4
    Building training set is easy
  • 1
    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

    Sign up to add or upvote consMake informed product decisions

    - 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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use OpenCV?
    What companies use Tesseract OCR?
    See which teams inside your own company are using OpenCV or Tesseract OCR.
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    What tools integrate with OpenCV?
    What tools integrate with Tesseract OCR?
      No integrations found

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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

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