Amazon Rekognition is a widely used image and video analysis tool. It uses deep machine learning models for identifying objects as images. It is a powerful, highly scalable solution that can analyze images quickly and offers numerous benefits. It can help you tag images along with real-time image and video processing. It's capable of identifying inappropriate video and image content, as well as performing face recognition and analysis. By using Amazon Rekognition Custom Labels, it is possible to differentiate between the objects and scenes in images specific to your business needs.
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The flexible and robust architecture of Contentstack allows you to integrate with any third-party application. And with Amazon Rekognition, Contentstack integrates seamlessly. The combined ability of Contentstack and Amazon Rekognition lets you create a system that is capable of assigning tags to images automatically. If you have a Contentstack-powered website, adding Amazon Rekognition to it is easy and can simplify and automate the process of tagging images.
Make Your Content More Searchable
The integration of Contentstack and Amazon Rekognition allows you to make your content searchable. Amazon Rekognition extracts metadata automatically from your assets stored in Contentstack. Using this metadata, your images and videos can be searched easily using keywords to get the exact asset.
Gain Valuable Insights Using Analytics
By integrating Contentstack with Amazon Rekognition, it is possible to perform analytics by assigning tags to Contentstack assets. You can easily add context to your assets and share the related information to your analytics tools. By analyzing categorization and usage of the data, you can get valuable insights and understand how your Contentstack assets impact engagement and lead to conversion.
Add Relevant Tags to Your Contentstack Assets
By integrating Contentstack with Amazon Rekognition, you can assign appropriate tags to your Contentstack assets automatically. This saves time and effort that's required in assigning them manually.