Data Annotation Services

Outsource Data Annotation Services to Access Relevant, Precise, and Accurately Labelled Data

Data Annotation Services

To outperform, global corporations are progressively incorporating self-learning Artificial Intelligence and Machine Learning (AI/ML) models into their operational frameworks. However, such models require secured, continuously flowing, and substantial amounts of relevant data for supervised training. This necessitates a meticulous and time-intensive process of annotating data, with two critical criteria to satisfy: quality and precision. Data annotation services by Srishta Technology ensure high-quality and precisely labelled datasets, helping businesses to effectively train their AI/ML models. As a top data annotation expert, Srishta Technology helps businesses with AI data annotation services to improve their ML algorithm predictions. Outsource data annotation services for ML to our data annotation specialists who have the right expertise to handle diverse data sets including images, videos, and text. We leverage different annotation methods for optimal adaptability and scalability of your AI-based training models. Being one of the seasoned data annotation partners, our annotation services are designed to meet the unique requirements of your ML project to provide high-volume, high-quality, structured data that can fuel your AI models on time

Computer Vision Innotation

 

We deliver precise computer vision solutions with our expert image and video dataset annotation services.

Natural Language Annotation

We transform your NLP projects with our comprehensive text and audio annotation services for accurate language processing

3D Point Cloud Annotation

We enhance your 3D models with our expert LiDAR and point cloud annotation services for precise object recognition.

Text is one of the most widely used data types in AI. , 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. 

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. Srishta Technology 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.

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.

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.

Label and categorize data that spans multiple formats, such as text, images, audio, and video, within a single dataset. Multimodal annotation enables AI models to process complex inputs across different media types.

Prepare multimodal data for AI applications such as:

  • Caption generation: Pair video, audio, and text to automatically generate captions for accessible content on television, social media, and more.
  • Gesture recognition: Label human gestures and facial expressions to enable virtual reality models to interpret non-verbal cues.
  • Multimodal search: Enable users to search via image, text, and voice for enhanced search relevance and product recommendations.

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.