Image annotation
We provide high quality image annotation and object recognition services. Our image annotation technology makes every picture focused and enables pixel-to-pixel measurements. We provide this service in both jpeg and dicom environments.
Multiple use cases:
- Training the machine to detect diseases using ML algorithms
- Understanding patient facial expressions
- And many more
Video annotation
We provide video annotation services to help train your ML algorithms accurately. This helps with precise identification of objects. Using our labeled videos with the exact metadata, the machine can easily differentiate between objects, in every frame of the video.
Multiple use cases:
- Identifying patients movements including walking, jogging, etc.
- Tracking surveillance footage for security purposes
- Understanding surgical videos
- And many more
Audio annotation
We use audio annotation to help make sound or speech recordings, of all formats, understandable to AI-powered machines. Our team explores various audio features and annotates the corpus with the right audio information. Each word is carefully assessed by our annotators in order to ensure that the recording is correctly annotated.
Multiple use cases:
- Hospitals building automated response system for customer care
- And many more
Text annotation
Through text annotation services we classify the text and assign appropriate tags with a predetermined set of categories. Through Natural Language Processing (NLP) we make text comprehensible for machines. We also help highlight important keywords and phrases for better understanding.
Multiple use cases:
- Identifying important sections in long documents
- Understanding medical text and doctor’s writing phrases better
- And many more
Semantic Annotation
Semantic annotation involves tagging concepts like people, places, or company names within a document to help ML models categorize new concepts in the future text. It is a critical component of AI training to improve chatbots and search relevance. Semantic annotation mainly involves tagging of key phrases and the appropriate identification parameters; it has a crucial role to play in-text annotation.
