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Multilingual Data Annotation Services company for Computer Vision

As a data annotation company we provide ethical and Multilingual data annotation services with Vetted, managed, experienced, IN HOUSE teams of expert annotators from 3 centers Worldwide for all your computer vision projects.

Trust by MANY AI companies looking for stable, cost effective & ethical operations

Oworkers > Data Annotation Services 

High-quality data annotation services company powered by a dedicated, multilingual in-house AI workforce, ensuring precise annotations for your image, video, and 3D point cloud data.

With 3 centers in the world in Bulgaria, Egypt and Madagascar Oworkers can adapt to any data annotation projects in 30 languages.

The different types of DATA annotation
for computer vision

images and objects annotations

Assigning descriptive tags or labels to entire images to categorize their content (e.g., “cat,” “car,” “indoor scene”).

bounding boxes annotation

Drawing rectangular boxes around objects to mark their location and boundaries.
This is fundamental for object detection tasks.

Polygon Annotation

Creating precise outlines around objects using multi-point polygons for more accurate object boundaries than bounding boxes.

3D Cuboid Annotation

Drawing three-dimensional boxes around objects, particularly useful for depth perception in autonomous driving.

semantic annotation services

Assigning a specific class label to every pixel in an image, grouping pixels of the same class without distinguishing individual instances.

Instance Segmentation

Instance Segmentation

Similar to semantic segmentation but distinguishes individual objects within the same class (e.g., separating each person in a crowd).

Panoptic Segmentation

Panoptic Segmentation

Combines semantic and instance segmentation to segment both “thing” classes (countable objects) and “stuff” classes (amorphous regions).

Keypoint and Landmark Annotation

Marking specific points on objects to identify features (e.g., facial landmarks, human pose joints).

Line and Spline Annotation

Line and Spline Annotation

Marking linear features like roads, lanes, or paths, essential for navigation systems.
Depth Annotation

Depth Annotation

Labeling the distance of objects from the camera, crucial for 3D scene understanding.

Why choose US
for your data annotation company ?

data-annotation-services-accuracy

ACCURACY

With technological tools from our multilingual data annotation company and a mix of QA (quality assurance) and QC (quality control) from our team processes, we deliver strong results with over 98% accuracy.

BIG COST SAVING

With our locations along the most cost effective in the world
Bulgaria / Madagascar and Egypt , you can save up to 80 % on your cost.

data annotation services low cost
data annotation services secure

SECURITY

We are ISO 27001 our full-time employees signed
NDA + work in office only in monitored facilities with strict security protocols + We are GDPR compliant

ETHICAL

We create positive social and economic change through employment in underserved communities.
We have strict code of ethics and code of conduct.

data annotation services ethical
data annotation services scalability

SCALABILITY

Even with In house workforce without use of crowdsourcing we can ramp up or down quickly and in large proportion
(+ 100 headcounts in less than 48 hours).

Expert

Industry Experience

Oworkers has over 12 years of experience. Our employee turnover is 1,7 % in 2024 Vertically segmented teams provide expertise into industry nuances.

best data annotation company

What is a good data annotation company ?

A good annotation company

  • Uses the best data annotation tools on the planet depending on the source type
  • Delivers very high productivity and accuracy rates guaranteed by strict SLA/KPI
  • Offers very scalable operation.
  • Offers very affordable pricing model,
  • Offers top data security protocols (ISO 27001).
  • Offers an ethical services, with a full compensation package for his employees
    (NO freelancers, NO crowdsourcing, NO subcontractors).

INDUSTRIES & SECTORS

Phone with the camera turned on

Retail & Ecommerce

Positioned security camera

Surveillance and digital identity

sectors transportation

Transportation & Shipping

sectors media

Media

Multiple linked vehicles

Adas + Autonomous Vehicle

Doctors performing surgery

Healthcare & Medtech

Robotic arm

Logistic and robotics

Person holding a phone and utilising multilingual data annotation services

Food, Agriculture and Live stocks

travel

Travel & Hospitality

Satellite overview of a town

Construction & Architecture

sectors gaming

Gaming

The word “insurance” surrounded by insurance logos
Banking, Financial Services and Insurance

Awards

Communication efficiency

logo-communication

We use Slack or meet or teams with a single point of contact
(your project manager)

Unlocking AI Potential Through Data Annotation Services

The Foundation of Machine Learning: Quality-Annotated Datasets

Data annotation services form the backbone of effective artificial intelligence development. At its core, data annotation is the process of enriching “raw” data to create “intelligent data” that can be understood by AI engines. This process of making data suitable for computer comprehension is referred to as data labeling. With the help of labeling, software programs can create associations and extrapolate those associations to actions.
For AI systems to function properly, particularly in computer vision applications, they must be trained on extensive datasets that humans have carefully labeled and categorized. These annotated datasets serve as the training ground for machine learning algorithms, enabling them to recognize patterns, identify objects, and make intelligent decisions. Without quality data annotation services, even the most advanced AI models would fail to deliver accurate results.

The Evolution of Annotation Methodologies in Computer Vision

Data annotation for computer vision has evolved significantly over the years. What began as simple image labeling has expanded to include sophisticated annotation techniques like semantic segmentation, instance segmentation, and 3D point cloud annotation. Today’s multilingual data annotation services provide comprehensive solutions for various annotation needs, from basic image classification to complex 3D cuboid annotation for autonomous driving applications.
Professional annotation capabilities span the full spectrum of computer vision needs, including bounding box annotation, polygon annotation, keypoint annotation, and semantic segmentation—all critical components for training robust AI models. As the field advances, data annotation companies continue to develop more efficient and accurate methodologies to meet emerging requirements.

Specialized Annotation Techniques and Multilingual Advantages

Precision Labeling for Advanced Computer Vision Applications

Modern data annotation companies must offer precision labeling techniques that align with specific industry requirements. For autonomous vehicle development, this might include lane marking annotations and 3D cuboid annotations that help AI systems understand depth and distance. For healthcare applications, precise segmentation of medical images enables AI to detect anomalies with greater reliability.
High-quality multilingual data annotation services maintain accuracy rates above 98%, leveraging a combination of cutting-edge technological tools and robust quality assurance processes. This precision is essential for computer vision systems where even small errors in annotation can lead to significant failures in real-world applications.

The Competitive Edge of Multilingual Annotation Teams

A multilingual data annotation company offers distinct advantages in an increasingly global AI landscape. When annotation teams can work across multiple languages, they bring cultural context and linguistic nuance to the annotation process. This becomes particularly valuable when annotating content that contains text elements or requires cultural understanding for accurate interpretation.
The best data annotation services employ teams capable of working in 25+ languages across different geographic locations. This multilingual capability ensures that data annotation for computer vision projects can accommodate regional variations and language-specific elements, resulting in more comprehensive and globally applicable AI models.

Evaluating Data Annotation Partners: Key Considerations

Technical Capabilities and Security Infrastructure

When evaluating data annotation companies, technical infrastructure and security protocols should be primary considerations. Look for partners with robust annotation platforms that support various data types and annotation formats, and ensure they maintain ISO certifications and GDPR compliance to protect sensitive data.
Top-tier multilingual data annotation services operate from secure facilities with ISO 27001 certification, implementing strict security protocols for all annotation work. Essential security features include biometric access controls, monitored facilities, and comprehensive NDA agreements with all staff members working on sensitive computer vision annotation projects.

Ethical Standards in the Annotation Workforce

The ethical treatment of annotation workers directly impacts the quality of data annotation services. Companies relying on exploitative practices or purely crowdsourced workers often struggle with consistency and quality issues. By contrast, data annotation companies that employ full-time annotators with good working conditions typically deliver superior results.
Ethical annotation providers employ full-time staff rather than freelancers or crowdworkers. Their annotators receive comprehensive benefits, including competitive salaries, healthcare benefits, social security coverage, and ongoing training opportunities. This ethical approach creates a stable and skilled workforce with remarkably low turnover rates. In OWorkers’ case, as low as 1.7% annually compared to industry averages exceeding 15%.

Long-term Adaptability to Evolving AI Requirements

The field of AI is rapidly evolving, requiring data annotation partners who can adapt to changing methodologies and requirements. Evaluate potential partners based on their track record of innovation and ability to scale operations as your needs grow.
Experienced data annotation services with a decade or more of industry presence demonstrate adaptability through continuous technological advancement and flexible scaling capabilities—ideally able to expand teams significantly within days when projects require additional resources.

The OWorkers Approach to Multilingual Data Annotation Excellence

OWorkers has established itself as a leading multilingual data annotation company by focusing on three core principles: quality, security, and ethical operations. Our in-house teams of expert annotators work from dedicated delivery centers across three countries, providing data annotation for computer vision projects in over 25 languages.
Their approach centers on employing full-time staff who undergo rigorous screening processes, including language proficiency checks and specialized training. This methodology ensures consistent quality across all data annotation services while creating a positive social impact.
With security-focused infrastructure including ISO 27001 compliance, GDPR adherence, biometric access controls, and comprehensive data protection protocols, OWorkers delivers multilingual annotation services that maintain both quality and security standards while offering significant cost advantages.

FAQ

How does annotation quality impact model performance?

The quality of data annotation directly determines AI model accuracy. Poor annotation creates a “garbage in, garbage out” scenario where even sophisticated algorithms cannot overcome fundamental data flaws. High-quality multilingual data annotation services ensure that AI models learn from correctly labeled data, resulting in more accurate predictions and decisions in real-world applications.

What security protocols protect sensitive visual data during annotation?

Effective data annotation companies implement multi-layered security measures, including physical access controls, secure digital environments, and strict confidentiality agreements. Industry-leading providers maintain comprehensive security protocols across all facilities, including ISO 27001 and GDPR compliance, biometric access controls, password management systems, and centralized antivirus and anti-malware solutions.

What level of domain expertise should annotation teams possess?

Different AI applications require varying levels of domain knowledge. Medical image annotation demands an understanding of anatomical structures, while automotive applications require familiarity with traffic scenarios. The best multilingual data annotation companies employ specialists with domain expertise or provide specialized training to ensure that annotators understand the context and requirements of each project.

User cases for Multilingual DATA ANnotation

Contact US and receive insights with the KPI
we achieve for our biggest clients.

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