Data Annotation Services

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Data Annotation and Tagging

Don't Be Split Between Train & Test

Even with several zettabytes of data generated daily, 90% of it is unstructured, presenting challenges for data science professionals seeking structured and tagged data to train their models.

Data annotation is the fundamental starting point for any successful model training, involving precise labeling and tagging of data to create informative datasets. However, this process can be arduous, especially at scale, requiring expertise and resources.

To overcome these hurdles, outsourcing data annotation emerges as a viable solution. By entrusting this task to specialized providers, data scientists can streamline their efforts, ensuring accurate annotations while focusing on refining models and extracting valuable insights from the data.

Outsourcing empowers data science professionals to leverage the full potential of data and drive AI innovation confidently.

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Data Annotation in Action

Data annotation is essential in training AI models for image and object recognition. Accurate labeling and tagging of images provide ground truth for algorithms to differentiate and identify objects.

It finds use in autonomous vehicles for detecting pedestrians, traffic signs, and obstacles. Facial recognition relies on data annotation to verify individuals for security applications, and in surveillance systems for activity detection.

Data annotation is pivotal in training NLP models to understand human language. Labeled text data enables sentiment analysis for gauging customer opinions.

Named Entity Recognition (NER) involves identifying entities like names and locations. Language translation benefits from annotated parallel corpora for accurate translations.

Annotated audio data is vital in training speech recognition systems, enabling them to convert spoken language into accurate text transcriptions.

Data annotation involves aligning audio recordings with their corresponding transcriptions, allowing AI models to learn the mapping between speech signals and textual representations. Speech recognition is integral in enhancing accessibility for individuals with visual impairments.

In the field of medical imaging, data annotation is indispensable. Annotated medical images serve as labeled examples to train AI models for tasks like tumor detection, organ segmentation, and disease classification.

By accurately annotating areas of interest in medical images, annotators provide the necessary information for AI models to assist healthcare professionals in identifying and diagnosing various conditions.

Data annotation is pivotal in training recommendation systems that provide personalized suggestions to users based on their preferences and behavior.

By annotating user interactions, such as clicks, purchases, and ratings, AI models can learn user preferences and patterns to recommend products, services, or content tailored to individual tastes.

Recommendation systems are also applied in content streaming services making it easier for users to discover new content aligned with their interests.
Data Annotation Uses
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Looking for a Data Annotation Partner?

Talk to LexiConn. We can put together a dedicated team to deliver large scale data annotation services for complex use cases.

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Everything is Data, Data is Everything

Data is all around us, and annotation plays a critical role in making sense of this data. However, there are challenges. From addressing bias and subjectivity to ensuring consistency and scalability, data annotation demands meticulous attention and expertise.

At LexiConn, we know a thing or two about overcoming these bottlenecks. In the past few years, we have supported India's leading OTT platform in tagging over 1.4 lakh videos, elevating their content recommendation system to new heights.

With our meticulous data annotation services, we empower AI-driven companies to overcome obstacles and achieve success in artificial intelligence. Want to know more?

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