Amazon Rekognition vs. Google Cloud Vision API

Amazon Rekognition is well suited for all image and videos analysis. Also, deep learning projects for image and video can easily be done using this. It is very easy to use so a beginner can also use it.

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Mostly Google Cloud Vision API is getting used for extracting values out of pdfs which are scanned images. We also tried using it in analyzing different images and their marketing campaign results based on different features it contains like color, objects, positioning, logos etc. Vision API could be used for in conjunction with LLM to provide a more detailed prompt for image generation.

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• Image tagging is very good.
• Object detection is precise.
• Video tagging has become very easy using Rekognition.

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• The cost is bit more for small scale companies.
• For text processing, OCR is not available in amazon rekognition.
• Only facial search is available in image search.

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Very much suitable for many applications where the image processing features are secondary and independent of any domain. This makes it a general solution and the recognition features are returned in a JSON object in response to the API called made which is a very simple process.

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It’s pretty easy to get started and the docs are good, plus it supports many features like OCR and image labeling. The accuracy is also generally good and it scale well for bigger use.
But the setup can be a bit confusing, especially with billing and permissions. and also the pricing is also not always very clear, and debugging issues sometimes take more time then expected.
Overall, it’s powerful tool but not the easiest for beginners.

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Support of Amazon is great. So far we are having a great experience using it.

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It's easy to implement and its documentation is great compared to CNTK.

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• The best thing it made our work fast.
• It reduces the chance of failure of a project which is based on image or video processing.
• We can assign anyone in our team to work on this as it very easy to use.

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