Artlangs owns serveral data processing centers, and has cooperating closely annotation resources including Outsourcing, Crowdsourcing.
Video annotation involves labeling sequences of images (frames) to train models for video analysis and recognition tasks. Improve computer vision capabilities for diverse applications across surveillance, autonomous navigation, social media, and AR/VR.
Video annotation tasks include:
Object Tracking: Annotate objects across multiple frames to enable dynamic scene analysis.
Action Recognition: Label actions and activities within videos for sports analytics, security, and more.
Event Detection: Identify and tag significant events in video footage for real-time applications.
Image annotation is one of the most vital responsibilities a computer has in the digital age. It is vital for training models for computer vision, facial recognition, and other visual AI applications. Appen provides detailed labeling of images to ensure precise and accurate model training.
Tackle use cases such as:
Object Detection: Identify and label objects within images for applications like autonomous driving and security systems.
Facial Recognition: Annotate facial features to improve identification and verification processes.
Image Classification: Label and categorize images for valuable use cases such as organizing an e-commerce product catalog and recommending content in a social media algorithm.
Train your model to understand the diversity of natural language, capturing the nuance of dialect and speaker demographics through highly accurate audio annotation. Audio annotation includes transcription and timestamping of speech data and can be applied to varied use cases – such as staging aggressive speech indicators and non-speech sounds like glass breaking for security and emergency applications.
Key capabilities include:
Speech Transcription: Convert spoken language in varied recording environments (e.g. multi-speaker, background noise) into text for analysis and model training.
Language and Dialect Identification: Annotate audio data to recognize different languages and dialects.
Speech Labelling: Label audio data with speaker information such as demographics, speech topic or emotion to enhance personalized AI applications.
Text is one of the most widely used data types in AI. Harness the power of Artlangs's global crowd for reliable text annotation services in over 230+ languages and diverse subject-matter expertise, ensuring your models understand and process natural language with high accuracy.
Types of text annotation include:
Sentiment Annotation: Assess attitudes, emotions, and opinions to provide valuable business insights, moderate content, and improve safety.
Intent Annotation: Categorize intent to make it easier for machines to understand the intent behind a query and route the request.
Semantic Annotation: Tag specific concepts within titles and search queries to train your algorithm to recognize key phrases and improve search relevance.
Named Entity Annotation: Detect critical information in large data sets with extensive manually annotated training data.
Artlangs owns serveral data processing centers, and has cooperating closely annotation resources including Outsourcing, Crowdsourcing.
Artlangs have professional professional project teams, including annotators and high-quality inspectors to undertake different kinds AI data.
Artlangs onws efficient and mature data annotation platform coverage speech,text,image and point clound.
Multiple ISO certifications ensure reliability and security of your data, and mature project process ensure qualified data delivery.
