331 already collected.
The Vision API can detect and extract text from images. There are two annotation features that support optical character recognition:
TEXT_DETECTION detects and extracts text from any image. For example, a photograph might contain a street sign or traffic sign. The JSON includes the entire extracted string, as well as individual words, and their bounding boxes.
DOCUMENT_TEXT_DETECTION also extracts text from an image, but the response is optimized for dense text and documents. The JSON includes page, block, paragraph, word, and break information.
Extract rich information from images to categorize and process visual data—and perform machine-assisted moderation of images to help curate your services.
Analyze an image
This feature returns information about visual content found in an image. Use tagging, domain-specific models, and descriptions in four languages to identify content and label it with confidence. Use Object Detection to get location of thousands of objects within an image. Apply the adult/racy settings to help you detect potential adult content. Identify image types and color schemes in pictures.
Recognize celebrities and landmarks
Recognize more than 200,000 celebrities from business, politics, sports and entertainment, as well as 9,000 natural and manmade landmarks from around the world.
Detect and compare human faces
Organize images into groups based on similarities
Identify previously tagged people in images
Run locally on-premises or in the cloud
Check the likelihood that two faces belong to the same person. The API will return a confidence score about how likely it is that the two faces belong to one person.
Detect one or more human faces in an image and get back face rectangles for where in the image the faces are, along with face attributes which contain machine learning-based predictions of facial features. The face attribute features available are: Age, Emotion, Gender, Pose, Smile, and Facial Hair along with 27 landmarks for each face in the image.
The Face API now integrates emotion recognition, returning the confidence across a set of emotions for each face in the image such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. These emotions are understood to be cross-culturally and universally communicated with particular facial expressions.
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