Data annotation for Computer Vision
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Image data annotation
3D Bounding Boxes / Cuboids 3D
Creating boxes around images to outline length, width, and approximate depth for your computer vision models
Our image annotators will categorize your images into the categories you specify.
Plugging in your AI models with people and object-identified image data for a better computer vision in retail, e-commerce, marketing, and advertising industries.
Draw pixel-perfect polygons around the critical factors to annotate your images. It can be used to train object detection and localization algorithms.
Classify each pixel in an image or video frame into the segments necessary for your machine vision algorithm to identify different entities.
Text data annotation
Optical Character Recognition (OCR) is taking all of the images of texts, regardless of whether they were printed, handwritten
or typed into editable text formats.
Classification and categorization
Text classification is assigning certain tags or categories depending on the content. There are wide uses of this technology for labeling topics,
detecting spam, and the intent. It could provide your business with valuable insights by extracting information from social media accounts,
customer service, customer feedback, and many other sources.
Named-entity recognition (tagging)
Named-entity recognition helps you find out which websites mention your company. In fact, it does not even have to be your company
but rather a product, employee name or any other entity.
Sentiment analysis helps your company understand customers’ opinions, appraisals, emotions, or attitudes towards a topic, person
or entity whether it is negative, positive or neutral.
Audio data annotation
Sound labeling involves the separation of all of the needed sounds and labeling them. These could be specific terms or the sound
of a specific musical instrument, for example.
Event tracking assesses the efficacy of sound event detection systems in multisource environments comparable to our daily lives,
where sound sources are rarely heard alone.
Speech to Text Transcription
Speech-to-text transcription is a crucial component of NLP development. It entails converting recorded audio
to text while meticulously categorizing both words and sounds that the speaker pronounces.
Listening to and analyzing audio recordings is known as audio classification. The machines can distinguish between noises
and speech commands using this information.
Video data annotation
2D Bounding Boxes
This annotation technique involves projecting a rectangular 2D box over each frame's target object, which assists the system
in recognizing the real things. This technology is commonly used in the automotive, security & entertainment, and media sectors
to analyze the footage.
3D boxes provide the algorithm with more understanding of the objects in the image, such as their length, width, and height.
It is frequently used to annotate movies for the automotive industry in order for the system to realize the traffic condition.
Polygons are great for annotating photos that are not even rectangular. It recognizes the object's actual shape and size,
as well as provides more precise localization. In the car industry, this type of labeling is utilized to identify all
of the obstacles on the street.
Labeling / Tagging
The entities in the frames are tagged or identified with data annotations. This educates the machine learning model on how
to recognize real-world items.
Classification / Categorization
This approach requires identifying or categorizing certain video occurrences. If you want your product to recognize specific gestures
or activities, this strategy is very useful.