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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.
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.
Pros of Amazon Rekognition
- Integrate easily with AWS4
Pros of Google Cloud Vision API
- Image Recognition9
- Built by Google7
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Cons of Amazon Rekognition
- AWS1