Data Annotation
Data annotation involves labeling unstructured data and information to train machine learning models. Currently, we find ourselves surrounded by large volumes of unstructured data. Raw data is presented in various formats such as images, videos, text, and audio.
Machine learning models can identify objects, analyze sentiment, and even perform tasks like driving and talking by labeling data appropriately.
Types of Data Annotation
The data annotation services offered by Skepar are tailored to fit each client’s unique needs. The most common annotation services we provide are text annotation, audio annotation, video annotation, and image annotation. With custom processes that ensure validation of annotation work, the Skepar Technologies teams work with clients to calibrate quality requirements.
Skepar Technologies Data Annotation Solution
Image Annotation
We provide image annotation services that include bounding boxes, LiDAR, polygon annotations, key point annotation, semantic annotation, semantic segmentation, and image classification. Every workflow ensures that every pixel of an image is of high quality.
Video Annotation
Experts at Skepar label video footage to train computers to identify and detect objects. Skepar web services video annotation solutions include bounding box, polygon, key point, and semantic segmentation that can be used in self-driving cars, drones, robotics, and more.
Text Annotation
It provides text annotation services such as sentiment analysis, intent analysis, named-entity recognition, and entity classification, which can be used in machine learning models like chatbots to understand text and metadata for natural language processing, sentiment analysis, spam detection, and intent detection, among others.
Audio Transcription
With Skepar, audio files are annotated in machine- and computer-readable formats. After the text data is categorized, tasks like named entity recognition, sentiment analysis, and conversation categorization can be performed.
Industries Using Annotation Today
Healthcare
Labeling radiographic scans, ultrasounds, CT scans, X-Rays, CT scans, MRI reports, and CT scans can assist in machine-learning projects in healthcare. Machine learning models are trained on the data to detect medical conditions and suggest treatments.
Finance
Workflows that visualize data annotations transform huge data sets from the financial and insurance sectors into insights directed at better customer experiences. Finance firms analyze financial data to detect market fluctuations and advise their clients.
Insurance
With machine learning models, insurance data annotation improves customer experiences. By analyzing complex behavior patterns created by AI models, risk assessment can be easier.
Autonomous Vehicles
Multi-sensor cameras capture pictures and video that must be expertly labeled before autonomous vehicles can be used. Based on these ground-truth datasets, vehicles can interact with and see the environment as humans do.
Social Media
Social media’s sentiment analysis and natural language processing allows companies to bypass counting likes and comments and instead gain insight into the opinions and emotions surrounding their brand, product, or service.
GIS Mapping services
Analyzing project risks and optimizing infrastructure planning and management are some of the many benefits of using GIS data annotation for GIS analytics and mapping.