OpenCV vs Tesseract OCR

Overview

OpenCV

Stacks1.4K

Followers1.1K

Votes102

Tesseract OCR

Stacks96

Followers286

Votes7

GitHub Stars70.7K

Forks10.4K

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.

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Advice on OpenCV, Tesseract OCR

Vladyslav

Sr. Directory of Technology at Shelf

Oct 25, 2019

Decided

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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Detailed Comparison

OpenCV	Tesseract OCR
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.	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.
C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android;More than 47 thousand people of user community and estimated number of downloads exceeding 7 million;Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics	-
Statistics
GitHub Stars -	GitHub Stars 70.7K
GitHub Forks -	GitHub Forks 10.4K
Stacks 1.4K	Stacks 96
Followers 1.1K	Followers 286
Votes 102	Votes 7
Pros & Cons
Pros 37 Computer Vision 18 Open Source 12 Imaging 10 Face Detection 10 Machine Learning	Pros 5 Building training set is easy 2 Very lightweight library Cons 1 Works best with white background and black text

What are some alternatives to OpenCV, Tesseract OCR?

Cloudinary

Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.

imgix

imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.

ImageKit

ImageKit offers a real-time URL-based API for image & video optimization, streaming, and 50+ transformations to deliver perfect visual experiences on websites and apps. It also comes integrated with a Digital Asset Management solution.

Google Cloud Vision API

Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.

Cloudimage

Effortless image resizing, optimization and CDN delivery. Make your site fully responsive and really fast.

scikit-image

scikit-image is a collection of algorithms for image processing.

Kraken.io

It supports JPEG, PNG and GIF files. You can optimize your images in two ways - by providing an URL of the image you want to optimize or by uploading an image file directly to its API.

ImageEngine

ImageEngine is an intelligent Image CDN that dynamically optimizes image content tailored to the end users device. Using device intelligence at the CDN edge, developers can greatly simplify their image management process while accelerating their site.

FFMPEG

The universal multimedia toolkit.

Amazon Rekognition

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.

Related Comparisons

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.

OpenCV vs Tesseract OCR

Overview

OpenCV vs Tesseract OCR: What are the differences?