Amazon Rekognition vs OpenCV

Overview

OpenCV

Stacks1.4K

Followers1.1K

Votes101

Amazon Rekognition

Stacks79

Followers152

Votes4

Amazon Rekognition vs OpenCV: What are the differences?

Introduction

In this markdown, we will be discussing the key differences between Amazon Rekognition and OpenCV, two popular computer vision tools.

Scalability: Amazon Rekognition is a cloud-based solution that can automatically scale to handle large volumes of image and video analysis tasks, making it suitable for applications that require high scalability. On the other hand, OpenCV is a library that runs on local machines, limiting its scalability to the hardware it is installed on.
Pre-built models: Amazon Rekognition provides pre-trained models for various computer vision tasks such as object and scene detection, facial analysis, and text recognition. These pre-built models enable developers to quickly integrate complex computer vision capabilities into their applications without the need for extensive training. OpenCV, on the other hand, does not provide pre-built models and requires developers to implement and train their own models from scratch.
Deep learning support: Amazon Rekognition utilizes deep learning algorithms for image and video analysis tasks. It leverages advanced neural network models to perform tasks such as object recognition and facial analysis with high accuracy. OpenCV, on the other hand, provides support for deep learning frameworks like TensorFlow and PyTorch, allowing developers to integrate their own trained models into their computer vision workflows.
Integration with other AWS services: Amazon Rekognition seamlessly integrates with other AWS services such as Amazon S3, Amazon DynamoDB, and Amazon CloudWatch. This integration enables developers to easily store and retrieve images and videos from Amazon S3, use Amazon DynamoDB for storing metadata associated with analyzed images, and monitor the analysis tasks through Amazon CloudWatch. OpenCV does not provide the same level of integration with cloud services and requires manual implementation for such functionalities.
Accuracy: Amazon Rekognition leverages advanced deep learning models and extensive training to provide high accuracy in computer vision tasks. It excels in tasks such as facial recognition and scene detection, and performs well even in challenging scenarios. OpenCV, while being a powerful computer vision library, may not achieve the same level of accuracy as Amazon Rekognition due to differences in the underlying models and training methods.
Pricing: Amazon Rekognition is a paid service that charges based on the number of images and videos processed, with additional fees for additional features like facial analysis. OpenCV, on the other hand, is an open-source library that is free to use, making it a cost-effective option for developers who do not require the scalability and additional features provided by Amazon Rekognition.

In summary, Amazon Rekognition offers greater scalability, pre-built models, deep learning support, integration with other AWS services, higher accuracy, but comes at a cost. OpenCV, while free to use, lacks the same scalability, pre-trained models, and integration with cloud services, but provides flexibility for developers to implement their own models.

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Advice on OpenCV, Amazon Rekognition

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	Amazon Rekognition
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.	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.
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
Stacks 1.4K	Stacks 79
Followers 1.1K	Followers 152
Votes 101	Votes 4
Pros & Cons
Pros 37 Computer Vision 18 Open Source 12 Imaging 10 Face Detection 10 Machine Learning	Pros 4 Integrate easily with AWS Cons 1 AWS

What are some alternatives to OpenCV, Amazon Rekognition?

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.

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.

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.

Related Comparisons

Amazon Rekognition vs OpenCV: What are the differences?

Introduction

In this markdown, we will be discussing the key differences between Amazon Rekognition and OpenCV, two popular computer vision tools.

Scalability: Amazon Rekognition is a cloud-based solution that can automatically scale to handle large volumes of image and video analysis tasks, making it suitable for applications that require high scalability. On the other hand, OpenCV is a library that runs on local machines, limiting its scalability to the hardware it is installed on.
Pre-built models: Amazon Rekognition provides pre-trained models for various computer vision tasks such as object and scene detection, facial analysis, and text recognition. These pre-built models enable developers to quickly integrate complex computer vision capabilities into their applications without the need for extensive training. OpenCV, on the other hand, does not provide pre-built models and requires developers to implement and train their own models from scratch.
Deep learning support: Amazon Rekognition utilizes deep learning algorithms for image and video analysis tasks. It leverages advanced neural network models to perform tasks such as object recognition and facial analysis with high accuracy. OpenCV, on the other hand, provides support for deep learning frameworks like TensorFlow and PyTorch, allowing developers to integrate their own trained models into their computer vision workflows.
Integration with other AWS services: Amazon Rekognition seamlessly integrates with other AWS services such as Amazon S3, Amazon DynamoDB, and Amazon CloudWatch. This integration enables developers to easily store and retrieve images and videos from Amazon S3, use Amazon DynamoDB for storing metadata associated with analyzed images, and monitor the analysis tasks through Amazon CloudWatch. OpenCV does not provide the same level of integration with cloud services and requires manual implementation for such functionalities.
Accuracy: Amazon Rekognition leverages advanced deep learning models and extensive training to provide high accuracy in computer vision tasks. It excels in tasks such as facial recognition and scene detection, and performs well even in challenging scenarios. OpenCV, while being a powerful computer vision library, may not achieve the same level of accuracy as Amazon Rekognition due to differences in the underlying models and training methods.
Pricing: Amazon Rekognition is a paid service that charges based on the number of images and videos processed, with additional fees for additional features like facial analysis. OpenCV, on the other hand, is an open-source library that is free to use, making it a cost-effective option for developers who do not require the scalability and additional features provided by Amazon Rekognition.

Amazon Rekognition vs OpenCV

Overview

Amazon Rekognition vs OpenCV: What are the differences?