
Create and configure a bot board, using commands to create a new board, set its name, description, and profile picture, and integrate it into your Telegram workflow.
Learn to build a reply bot by setting up a client, running the app, and examining the message exchange and service responses in the chat log.
Learn to set up a Microsoft cognitive services account, obtain a Computer Vision API key, and integrate image analysis to extract data from a photo via a URL.
Analyze data by faces using a lab library, examine dogs, and obtain adult and racist scores, then use this information in your application via the console.
Explore building an ai and machine learning recognition app by implementing a computer vision pipeline that detects faces and estimates ages from images.
Explore computer vision techniques and api-driven image analysis to recognize labels, extract text, and analyze video content for building an ai and machine learning recognition application.
Explore face detection with computer vision to identify emotion, gender, age, and glasses in images containing more than one person. Learn how to connect Microsoft cognitive services to your application.
Explore text detection with Google OCR through the Google Drive API endpoint and post requests to extract image text. Learn how the text detection response presents data about the image.
Learn to set up Google Cloud Platform for AI in medicine by creating a project, generating API credentials, downloading the service account key, and enabling billing.
Start by configuring Google Cloud credentials to run the OCR bot, perform image detection, and extract text annotations and descriptions from images.
Explore how to use Google Cloud Vision text detection to extract text from images, enable the Vision API, and authenticate requests with credentials, including cost considerations.
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.
Computer Vision
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.
Face
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
Face verification
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.
Face detection
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.
Emotion recognition
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.