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Amazon Rekognition vs TensorFlow: What are the differences?

Amazon Rekognition and TensorFlow are two powerful tools used in the field of computer vision and machine learning. Here are the key differences between them:

  1. Platform and Vendor: Amazon Rekognition is a cloud-based image and video analysis service provided by Amazon Web Services (AWS), while TensorFlow is an open-source machine learning framework developed by Google. Amazon Rekognition offers a fully managed solution with pre-trained models, whereas TensorFlow provides a more flexible and customizable environment for building and training models.

  2. Use Case and Accessibility: Amazon Rekognition is designed for users who need quick and easy access to pre-trained models for tasks like image and video analysis, facial recognition, and object detection. TensorFlow, on the other hand, caters to developers and researchers who require more control and customization over their machine learning models. It is widely used for training complex neural networks and handling a wide range of machine learning tasks.

  3. Model Training: With Amazon Rekognition, the model training process is abstracted away, and users mainly work with pre-trained models. This makes it suitable for scenarios where model training is not the primary focus. TensorFlow, being a deep learning framework, provides comprehensive support for model training, fine-tuning, and transfer learning. It empowers developers to build custom models or modify existing ones to suit their specific needs.

  4. Integration and Deployment: Amazon Rekognition is tightly integrated with other AWS services, making it easy to incorporate image analysis capabilities into AWS-based applications. On the other hand, TensorFlow is more versatile in terms of deployment options. It can be deployed on-premises, on the cloud, or even on edge devices, providing more flexibility for various deployment scenarios.

  5. Cost and Pricing: Amazon Rekognition follows a pay-as-you-go pricing model, where users are billed based on their usage of the service. TensorFlow, being open-source, does not have any licensing costs. However, the total cost of using TensorFlow depends on factors such as hardware, cloud infrastructure, and developer expertise needed for model development and deployment.

In summary, Amazon Rekognition is a user-friendly cloud service that offers pre-trained models and simplified image and video analysis, while TensorFlow is a powerful open-source framework that provides more control and flexibility for building and training custom machine learning models.

Decisions about Amazon Rekognition and TensorFlow
Vladyslav Holubiev
Sr. Directory of Technology at Shelf · | 1 upvote · 43.3K views

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 Amazon Rekognition
Pros of TensorFlow
  • 4
    Integrate easily with AWS
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5

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Cons of Amazon Rekognition
Cons of TensorFlow
  • 1
  • 9
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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- No public GitHub repository available -

What is 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.

What is 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.

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Dec 4 2019 at 8:01PM


What are some alternatives to Amazon Rekognition and TensorFlow?
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.
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.
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.
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.
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.
See all alternatives