What is Computer Vision?
TLS 1.2 is now enforced for all HTTP requests to this service. For more information, see Azure Cognitive Services security.
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Optical Character Recognition (OCR)
Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. It uses deep learning based models and works with text on a variety of surfaces and backgrounds. These include business documents, invoices, receipts, posters, business cards, letters, and whiteboards. The OCR APIs support extracting printed text in several languages. Follow a quickstart to get started.
Computer Vision for digital asset management
Computer Vision can power many digital asset management (DAM) scenarios. DAM is the business process of organizing, storing, and retrieving rich media assets and managing digital rights and permissions. For example, a company may want to group and identify images based on visible logos, faces, objects, colors, and so on. Or, you might want to automatically generate captions for images and attach keywords so they're searchable. For an all-in-one DAM solution using Cognitive Services, Azure Cognitive Search, and intelligent reporting, see the Knowledge Mining Solution Accelerator Guide on GitHub. For other DAM examples, see the Computer Vision Solution Templates repository.
Analyze images for insight
You can analyze images to provide insights about their visual features and characteristics. All of the features in the table below are provided by the Analyze Image API. Follow a quickstart to get started.
Tag visual features
Identify and tag visual features in an image, from a set of thousands of recognizable objects, living things, scenery, and actions. When the tags are ambiguous or not common knowledge, the API response provides hints to clarify the context of the tag. Tagging isn't limited to the main subject, such as a person in the foreground, but also includes the setting (indoor or outdoor), furniture, tools, plants, animals, accessories, gadgets, and so on. Tag visual features
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process further relationships between the objects in an image. It also lets you know when there are multiple instances of the same tag in an image. Detect objects
Identify commercial brands in images or videos from a database of thousands of global logos. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement. Detect brands
Categorize an image
Identify and categorize an entire image, using a category taxonomy with parent/child hereditary hierarchies. Categories can be used alone, or with our new tagging models.
Currently, English is the only supported language for tagging and categorizing images. Categorize an image
Describe an image
Generate a description of an entire image in human-readable language, using complete sentences. Computer Vision's algorithms generate various descriptions based on the objects identified in the image. The descriptions are each evaluated and a confidence score generated. A list is then returned ordered from highest confidence score to lowest. Describe an image
Detect faces in an image and provide information about each detected face. Computer Vision returns the coordinates, rectangle, gender, and age for each detected face.
Computer Vision provides a subset of the Face service functionality. You can use the Face service for more detailed analysis, such as facial identification and pose detection. Detect faces
Detect image types
Detect characteristics about an image, such as whether an image is a line drawing or the likelihood of whether an image is clip art. Detect image types
Detect domain-specific content
Use domain models to detect and identify domain-specific content in an image, such as celebrities and landmarks. For example, if an image contains people, Computer Vision can use a domain model for celebrities to determine if the people detected in the image are known celebrities. Detect domain-specific content
Detect the color scheme
Analyze color usage within an image. Computer Vision can determine whether an image is black & white or color and, for color images, identify the dominant and accent colors. Detect the color scheme
Generate a thumbnail
Analyze the contents of an image to generate an appropriate thumbnail for that image. Computer Vision first generates a high-quality thumbnail and then analyzes the objects within the image to determine the area of interest. Computer Vision then crops the image to fit the requirements of the area of interest. The generated thumbnail can be presented using an aspect ratio that is different from the aspect ratio of the original image, depending on your needs. Generate a thumbnail
Get the area of interest
Analyze the contents of an image to return the coordinates of the area of interest. Instead of cropping the image and generating a thumbnail, Computer Vision returns the bounding box coordinates of the region, so the calling application can modify the original image as desired. Get the area of interest
Moderate content in images
You can use Computer Vision to detect adult content in an image and return confidence scores for different classifications. The threshold for flagging content can be set on a sliding scale to accommodate your preferences.
Deploy on premises using Docker containers
Use Computer Vision containers to deploy API features on-premises. These Docker containers enable you to bring the service closer to your data for compliance, security or other operational reasons. Computer Vision offers the following containers:
- The Computer Vision read OCR container (preview) lets you recognize printed and handwritten text in images.
- The Computer Vision spatial analysis container (preview) lets you to analyze real-time streaming video to understand spatial relationships between people and their movement through physical environments.
Computer Vision can analyze images that meet the following requirements:
- The image must be presented in JPEG, PNG, GIF, or BMP format
- The file size of the image must be less than 4 megabytes (MB)
- The dimensions of the image must be greater than 50 x 50 pixels
- For the Read API, the dimensions of the image must be between 50 x 50 and 10000 x 10000 pixels.
Data privacy and security
As with all of the Cognitive Services, developers using the Computer Vision service should be aware of Microsoft's policies on customer data. See the Cognitive Services page on the Microsoft Trust Center to learn more.
Get started with Computer Vision by following the quickstart guide in your preferred development language: