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Data Annotation

Fueling Innovation in AI and Machine Learning

Data is an integral part of all machine learning and deep learning algorithms.

Building an AI or MI models that act like humans requires a large volume of training data. A reliable AI model needs algorithms with appropriately structured and labelled data.

HOW WE DO IT

Our Working Process for Data Annotation:

Project Analysis:

We begin by thoroughly analysing your project requirements and understanding your data annotation needs. This includes assessing the data type, annotation goals, and any specific guidelines or criteria you provide.

Annotation Strategy

Based on the project analysis, we develop a tailored annotation strategy that outlines the annotation techniques, tools, and workflows. Our experienced team selects the most suitable annotation methods to ensure accuracy and efficiency.

Annotation Tool Selection

We carefully choose the right annotation tools and software platforms to meet the specific requirements of your project. These tools facilitate annotation and allow for seamless collaboration and quality control.

Annotation Guidelines

We develop comprehensive annotation guidelines that serve as a reference for our annotators. These guidelines cover specific labelling conventions, annotation instructions, and quality assurance protocols to ensure consistent and accurate annotations.

Annotation Execution

Our skilled annotators begin the annotation process according to the defined strategy and guidelines. They meticulously annotate the data, applying the chosen techniques such as bounding boxes, semantic segmentation, object tracking, or text annotation, depending on the nature of the project.

Quality Control

We have a rigorous quality control process to maintain high annotation accuracy standards. Our dedicated quality assurance team conducts thorough reviews, spot checks, and cross-validations to ensure the annotations meet your expectations and adhere to the defined guidelines.

Iterative Feedback

We value your input and encourage a collaborative approach. Throughout the annotation process, we maintain open lines of communication, providing regular updates and seeking your feedback. Any clarifications or adjustments required are addressed promptly to ensure the desired output.

Data annotation is labelling unstructured data and information to train machine learning models. It is the categorisation and labelling of data for AI applications in various formats like text, images, audio and videos. Appropriately labelled data is used to help supervised machine learning models identify objects, understand the sentiment and perform functions like speech recognition or even driving. The result is an enhanced customer experience.

Types of Annotation

Image annotation

Image annotation is vital for various applications, including computer vision, robotic vision, facial recognition and solutions that rely on machine learning to interpret images. Many use cases require high volumes of annotated images, from computer vision systems used by self-driving vehicles and machines that pick and sort products to healthcare applications that auto-identify medical conditions. Image annotation increases precision and accuracy by effectively training these systems.

Video annotation

Video annotation is labelling or tagging video clips for training computer vision models to detect or identify objects. Unlike image annotation, video annotation involves annotating objects on a frame-by-frame basis to make them recognisable for machine learning models.

Text annotation

The most commonly used data type is text. According to a recent AI and MI learning report survey,70% of companies rely on text. Text annotations include a wide range of annotations like sentiment, intent and query.

Audio annotation

Audio annotation is the transcription and time-stamping of speech data, including the transcription of specific pronunciation and intonation, along with the identification of language, dialect and speaker demographic depending on the use case, as some require a very specific approach.

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