Amazon Rekognition vs Google Cloud Vision API

Overview

Google Cloud Vision API

Stacks139

Followers276

Votes16

Amazon Rekognition

Stacks79

Followers152

Votes4

Amazon Rekognition vs Google Cloud Vision API: What are the differences?

Introduction

Amazon Rekognition and Google Cloud Vision API are two popular computer vision services that provide image and video analysis capabilities. While both services offer similar functionalities, there are several key differences between them. This article aims to highlight these differences in order to help users make an informed decision when choosing between the two.

Pricing model: Amazon Rekognition and Google Cloud Vision API have different pricing models. Amazon Rekognition charges users based on the number of API calls, the amount of data processed, and the storage used. On the other hand, Google Cloud Vision API has a tiered pricing structure that takes into account the number of features requested, such as label detection or face detection.
Customization options: Amazon Rekognition allows users to create custom models based on their specific use cases. This feature enables users to train the system to recognize specific objects or entities that are relevant to their applications. In contrast, Google Cloud Vision API does not currently offer custom model training, limiting the level of customization that users can achieve.
Supported platforms: While both services can be used in various programming languages and platforms, Amazon Rekognition provides SDKs (Software Development Kits) for a wider range of platforms, including mobile platforms like iOS and Android. Google Cloud Vision API, on the other hand, has SDKs available for popular programming languages but does not have dedicated SDKs for mobile platforms at the time of writing.
Integration with other services: Amazon Rekognition seamlessly integrates with other AWS (Amazon Web Services) services, such as Amazon S3 (Simple Storage Service) for storing and retrieving images and videos. It also integrates well with Amazon Kinesis Video Streams for real-time streaming analysis. In comparison, Google Cloud Vision API integrates with other Google Cloud Platform services, such as Google Cloud Storage for image storage and Google Cloud Pub/Sub for real-time messaging.
Supported image formats: Amazon Rekognition supports a wide range of image formats, including JPEG, PNG, BMP, and GIF, allowing users to analyze images in different formats. In contrast, Google Cloud Vision API primarily supports JPEG and PNG formats, limiting the types of images that can be processed.
Text extraction capabilities: When it comes to text extraction from images, Amazon Rekognition provides more advanced capabilities. It can detect text in images and also extract text embedded in the image itself, such as text within signs or labels. Google Cloud Vision API, on the other hand, focuses more on general text detection rather than extracting text from specific image elements.

In summary, Amazon Rekognition and Google Cloud Vision API differ in terms of pricing model, customization options, supported platforms, integration with other services, supported image formats, and text extraction capabilities. These differences highlight the unique strengths of each service, allowing users to choose the one that best aligns with their specific requirements.

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Advice on Google Cloud Vision API, 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

Google Cloud Vision API	Amazon Rekognition
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.	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.
Powerful Image Analysis; Insight From Your Images; Detect Inappropriate Content; Image Sentiment Analysis; Extract Text	-
Statistics
Stacks 139	Stacks 79
Followers 276	Followers 152
Votes 16	Votes 4
Pros & Cons
Pros 9 Image Recognition 7 Built by Google	Pros 4 Integrate easily with AWS Cons 1 AWS

What are some alternatives to Google Cloud Vision API, Amazon Rekognition?

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.

Tesseract.js

This library supports over 60 languages, automatic text orientation and script detection, a simple interface for reading paragraph, word, and character bounding boxes. Tesseract.js can run either in a browser and on a server with NodeJS.

Image to Prompt AI

Free AI-powered image to prompt generator. Upload images and get detailed prompts for AI art generation with our advanced converter.

Free AI Image Detector

Is this image AI-generated? Free AI detector with 99.7% accuracy detects fake photos, deepfakes, and AI images from DALL-E, Midjourney, Stable Diffusion. No signup required.

SAM 3D

Meta's SAM 3D brings human-level 3D perception to computer vision. Reconstruct objects and bodies from single images with unprecedented accuracy and speed.

libpng

It is the official Portable Network Graphics (PNG) reference library. It is a platform-independent library that contains C functions for handling PNG images. It supports almost all of PNG's features, is extensible, and has been widely used and tested.

OpenJPEG

It is an open-source JPEG 2000 codec written in C language.

ZXing

It is a barcode scanning library for Java, Android. Decode a 1D or 2D barcode from an image on the web.

EasyOCR

It is ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai.

libjpeg

It is a free library for JPEG image compression.

Related Comparisons

Amazon Rekognition vs Google Cloud Vision API: What are the differences?

Introduction

Pricing model: Amazon Rekognition and Google Cloud Vision API have different pricing models. Amazon Rekognition charges users based on the number of API calls, the amount of data processed, and the storage used. On the other hand, Google Cloud Vision API has a tiered pricing structure that takes into account the number of features requested, such as label detection or face detection.
Customization options: Amazon Rekognition allows users to create custom models based on their specific use cases. This feature enables users to train the system to recognize specific objects or entities that are relevant to their applications. In contrast, Google Cloud Vision API does not currently offer custom model training, limiting the level of customization that users can achieve.
Supported platforms: While both services can be used in various programming languages and platforms, Amazon Rekognition provides SDKs (Software Development Kits) for a wider range of platforms, including mobile platforms like iOS and Android. Google Cloud Vision API, on the other hand, has SDKs available for popular programming languages but does not have dedicated SDKs for mobile platforms at the time of writing.
Integration with other services: Amazon Rekognition seamlessly integrates with other AWS (Amazon Web Services) services, such as Amazon S3 (Simple Storage Service) for storing and retrieving images and videos. It also integrates well with Amazon Kinesis Video Streams for real-time streaming analysis. In comparison, Google Cloud Vision API integrates with other Google Cloud Platform services, such as Google Cloud Storage for image storage and Google Cloud Pub/Sub for real-time messaging.
Supported image formats: Amazon Rekognition supports a wide range of image formats, including JPEG, PNG, BMP, and GIF, allowing users to analyze images in different formats. In contrast, Google Cloud Vision API primarily supports JPEG and PNG formats, limiting the types of images that can be processed.
Text extraction capabilities: When it comes to text extraction from images, Amazon Rekognition provides more advanced capabilities. It can detect text in images and also extract text embedded in the image itself, such as text within signs or labels. Google Cloud Vision API, on the other hand, focuses more on general text detection rather than extracting text from specific image elements.

Amazon Rekognition vs Google Cloud Vision API

Overview

Amazon Rekognition vs Google Cloud Vision API: What are the differences?