DATA ANNOTATION

We prepare data input for AI systems to be trained intelligently. 

Building an AI or Machine Learning model that acts like a human requires large volumes of properly categorized and annotated training data. Text, audio, image, or video must become trained data through Data Annotation. 

These tasks are typically completed by humans who must manually label and classify objects. We provide a team of specialized data annotators, reliable crowd contributors, and AI-assisted tools to provide high-quality training data.

OUR DATA ANNOTATION SERVICES

BOUNDING BOX BACKGROUND DATA ANNOTATION

BOUNDING BOX

Bounding box improves machine learning algorithms’ ability to detect and classify the object that they’re required to look for. Our highly-trained Computer Vision teams have the best practices for annotating with bounding boxes for faster and more accurate labeled objects.

This technique has various applications, but one of its popular uses is for detecting different object statuses or patterns. Some of the bounding box common use cases are insurance claims for car accidents, object detection for self-driving cars, detecting the progress of viruses and bacteria for the healthcare industry, and image labeling for e-commerce and retail.

POLYGON

For labeling objects with irregular shapes, polygon annotation guarantees a pixel-perfect annotation with no irrelevant objects ruining the quality of the annotated area.

Polygon is applied to detect objects with complex shapes for high accuracy data, such as human poses in sports analytics, architectures & buildings, detect objects for drone and satellite imagery, and other objects of your interest. 

KEYPOINT

Key point annotation helps annotators to mark the main part or the “key” locations on an image using different dots in annotation tools. This supports the machine to differentiate between similar objects.

Thus, keypoint annotation is an effective approach to tracking variations between objects that usually appear with the same structure (human figures, facial features, and buildings).

CUBOID

With 3D cuboid annotation, annotators can measure the depth of specific objects like vehicles (motorbikes, cars, trucks, etc.) with precise dimensions and attributes. Cuboid “teaches” machine learning algorithms to visualize 3D simulated versions of 2D images captured by cameras

This technique of annotating enables AI machines to better recognize furniture and infrastructures in construction. Automobile and warehouse industries apply cuboid techniques to help their robots become familiar with objects in reality.

TEXT ANNOTATION BACKGROUND

TEXT ANNOTATION

For text annotation, a metadata tag is used to label the characteristics of a dataset. Tag criteria like phrases, sentences, keywords, and sentiments in the text will provide valuable data to train the machine about human writing intent and emotions in each word.

Text annotation is best applied for creating smart chatbots, voice assistants, more efficient search engines, machine translators, archiving historical documents, and more.

BOUNDING BOX

We have a set of best practices for annotating with bounding boxes conducted by our highly-trained Computer Vision teams for faster and more accurate labeled objects.

POLYGON

For labeling objects with irregular shapes, polygon annotation guarantees a pixel-perfect annotation with no irrelevant objects ruining the quality of the annotated area.

KEYPOINT

Keypoint annotation is an effective approach to tracking variations between objects that usually appear with the same structure (human figures, facial features, and buildings).

CUBOID

With 3D cuboid annotation, annotators can measure the depth of specific objects like vehicles (motorbikes, cars, trucks, etc.) with precise dimensions and attributes.

OBJECT DETECTION

Utilize the power of Computer Vision, we train the machine with high-quality data, making the robots familiar with construction and interior items. The client’s system can now automatically update the status of construction projects, saving operation time and costs. 

TEXT ANNOTATION

TEXT ANNOTATION BACKGROUND

For text annotation, we use a metadata tag to label the characteristics of a dataset. Tag criteria like phrases, sentences, keywords, and sentiments in the text will provide valuable data to train the machine about human writing intent and emotions in each word.

BENEFITS

01

DATA SECURITY

Confidential information will remain secure and be restricted from outside exposure with a reliable information security management system (ISMS) based on the ISO 27001 standard with GDPR compliance.

02

ON-DEMAND WORKFORCE

Handle peak time and large-scale projects with an on-demand workforce by our young & enthusiastic Vietnamese data annotators. We build a team specialized in scalable projects with a quick turnaround time to meet different clients’ needs.

03

QUALITY ASSURANCE

Service quality is managed by our Quality Control team who makes sure each project is carried out with the highest level of professionalism.

RELEVANT INDUSTRIES

CASE STUDIES

Straight-Through Process for Customer Onboarding Background

Straight-Through Process for Customer Onboarding

An automatic solution when it comes to no manual intervention involved and driving operational efficiency
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Automated Insurance Claims Background

Automated Insurance Claims

Intelligent automation solution to reduce complex claims document processing time from days to minutes.
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Digital Inspection System Background

Digital Inspection System

Spend less time collecting data from paperwork and more time improving your inspection performance.
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