Image-to-text

Enhance content management with general-purpose visual and language understanding

Overview

Bridging the gap between visual and textual content is a crucial step in unlocking the full potential of digital assets. The Image-to-text ML model is an advanced solution designed to do just that by providing general-purpose visual and language understanding.

The model leverages state-of-the-art natural language processing and computer vision techniques to facilitate the understanding of images and textual data. When a user submits an image and an accompanying textual prompt (typically in the form of a question regarding the image), the model processes the visual and textual data, identifying objects, context and relationships within the image, and generates a relevant response.

Users can pose a wide range of questions, from object recognition and content analysis to more complex queries related to the image. The output is a properly constructed natural language answer that provides insights or information pertaining to the submitted data.

Our Image-to-text functionality is a versatile tool that gives customers the ability to extract insights, enrich content and enhance the overall management of digital assets.

Typical use cases

The Image-to-text functionality is powerful enough to be applied across a spectrum of industries and domains, such as:

• Content tagging - Customers can automatically generate descriptive metadata for images, simplifying the organization and retrieval of digital assets.

• E-commerce and product catalogs - E-commerce platforms can utilize the model to answer user queries about product images, providing detailed information and enhancing the shopping experience.

• Media and entertainment - Media companies can analyze and describe scenes, characters and objects in images, aiding in content categorization and analysis.

• Educational content - Educational institutions can enhance e-learning platforms by automatically generating explanations and descriptions for visual content in course materials.

API endpoints

Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:

Scaleflex API for Digital Asset Management (DAM), Visual AI and Media OptimizationScaleflex API

There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.

Example API responses

Input image	Input prompt	API response
	{ "prompt": "Is the car moving fast?" }	{ "status": "success", "version": "3.0.2", "image_to_text": "No, the car is not moving fast. It is parked in a grassy field.", "file_downloaded": "it_tesla.jpeg", "request_uuid": "5145e03f-78f0-4667-a867-72d955c2d4bd", "sha1": "cc9a4427c90ba5881e5be8d3521724cfcfc9f5b8" }
	{ "prompt": "Describe the picture" }	{ "status": "success", "version": "3.0.2", "image_to_text": "The picture is a black and white photograph of a canal in a city, featuring a bridge with a clock tower in the background. The canal is filled with water, and there are boats floating on it. The scene is set in a European city, with a large building and a clock tower visible in the background. The image captures the charm and beauty of the city's waterways and architecture.", "file_downloaded": "it_amsterdam.jpeg", "request_uuid": "cb456045-f63f-4b71-9d22-c9b8f9e6eb42", "sha1": "00ceb83e817872c6b2ef103478f62af7eac3dc8e" }