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Image Annotation Services

Services We Deliver

Klatch's Image Annotation Services

We support clients with all image labeling needs, such as collecting and developing AI training datasets for computer vision systems and NLP solutions. Our goal is to enhance and annotate images precisely and securely, enabling your data projects to succeed.

Bounding Boxes

It is the most often used sort of image annotation in computer vision. Klatch computer vision professionals employ rectangular box annotation to represent things and train data, allowing algorithms to recognize and locate items using annotated photos throughout the machine learning process. The simplicity of bounding boxes is its strength, making this image annotation approach ideal for many applications.

Polygon Annotation

Expert annotators mark each vertex of the target object with a point. Polygon annotation allows you to mark all of the precise edges of an object, independent of form. It enables computer vision and other AI models to recognize and respond to objects. This approach is valuable in computer vision since it allows annotators to detect irregular forms, allowing computers to recognize and respond to them.

3D Cuboid Annotation

Klatch annotators can build training datasets for machine learning models to identify the depth of objects by using cuboids. Expert data labeling generates best-in-class training datasets for computer vision algorithms to determine the dimensions of objects and obstacles. These dots are then joined with a line, resulting in a 3D depiction of the thing, using anchor points commonly placed at the boundaries of an item.

Semantic Segmentation

Klatch team segments images into components, which are subsequently annotated. Our annotation specialist discover desirable things at the pixel level within photos. Data are arranged in many formats for AI models across various use cases using expert semantic segmentation.

Polyline Annotation

Annotation specialists at Klatch employ polyline annotation to build training datasets, which teach a machine-learning model to detect physical boundaries and function inside those bounds. One of the most common uses is teaching traffic restrictions to autonomous vehicles.

Image Classification

Klatch annotators categorize imagery or objects inside photographs using proprietary multi-level taxonomies such as land use, crops, and residential property attributes. Image data is transformed into image insights via expert image categorization, which is then used by AI and ML algorithms.

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Industries we serve

Industries benefiting from our Image Labeling solutions

Our Image Annotation services are used across various sectors that use AI or machine learning-based models. We cover all industries, from retail to institutions and healthcare, with the same degree of attention and quality.

Autonomous Transportation
Klatch’s data annotation specialists provide high-quality training data that supports the machine learning models developed by autonomous transportation and technology firms.
Healthcare
Klatch provides pharmaceutical, medical device, health insurance, and network providers with HIPAA-compliant image annotation and collection solutions.
Financial Services
Klatch helps financial institutions utilise machine learning models and RPA for greater productivity, customer experiences, and risk assessment.
Commerce
Katch delivers customer experience (CX) solutions to B2B, B2C, and D2C organisations to improve customer service, market research, and brand reputation.
Social Media
Klatch delivers UGC moderation, profanity filtration, text, image, and video moderation, and social media monitoring to improve brand reputation and user experience.
Geospatial Technology
Klatch draws insight from remote sensing datasets such as satellite, aerial, and drone photography to feed AI technologies in energy, agriculture, logistics, security, and mapping.

why choose us

Why Klatch is the right partner

Our image annotation propels you from experimental R&D prototypes to operation, production-ready solutions. We understand that your data training process is dynamic, and we strive to be as agile and adaptable as you require.

Quality with Accuracy
Get the best-in-class quality services with the highest accuracy level, delivering excellent data annotation through multiple stages of auditing and reviewing labeled data.
Fully Scalable Workforce
Work with an expert-degree prolonged workforce to annotate data as consistent with the call for a scalable answer with a turnaround time to satisfy the unique client’s needs.
Cost-Effective Pricing
Outsourcing data annotations means that clients have access to cost-effective data annotation services that help minimize project costs and help them with the highest efficiency.
Security with Privacy
We are certified and manage employees worldwide, all under strict NDA. We perform in monitored facilities for high-protection work, with strict protection protocols.

delivery

Image Annotation process

Our specialists and technology enable your project with capacity control, real-time quality monitoring, secured access, and streamlined team collaboration.

Find out more

Expert Consultation
Dynamic, solution-oriented strategy. Problem-solving through entensive annotation.
Training
Targeted resources. Skilled specialists. Focused and in-depth formative assessment program. Domain knowledge. Drafting tools.
Process Customization
Annotation tools and procedures must be in sync. Development milestones are structured on production processes and quality assurance in multiple phases.
Feedback Loop
Analytics focused on providing transparency. Insights on service delivery and real-time tracking of the project. Model enhancement using dynamic simulation.
Assessment
Deliverable assessment. Key metric assessment, as well as quality control procedures. Rethinking the model. Analyze business outcomes.

FAQ

Frequently asked questions about image annotation

What is meant by image annotation?

Image annotation means the process of classifying an image using human-powered labor and computer assistance. It is a necessary stage in developing computer vision models for tasks such as image segmentation, image classification, and object identification. Image annotation can range from labeling every group of pixels to labeling the whole image with a single label. High-quality annotation is the foundation of successful image annotation initiatives employing computer vision. The sort of annotation required will be determined by the use case for which the project is created.

What is image annotation example?

5 common image annotation examples are:

Bounding Boxes: Development of self-driving cars are an image annotation example. Klatch annotators marked vehicles, bikes, and people in traffic. The annotated images are placed into a machine-learning model to help the self-driving car tell them properly.
3D cuboids: Klatch annotators draw a cube around the object of interest and put anchor points at each object’s edges. The annotator estimates where the edges would be based on the size and height of the object as well as the angle of the image.
Polygons: Klatch annotators draw lines by putting dots around the outside edge of the object of interest. The space between the dots is labeled with classes, such as cars, bikes, or lorries.
Semantic segmentation: Klatch annotators are handed a person’s image and told to label each body structure with the correct name or to separate road infrastructure into vehicles, buildings, pedestrians, barriers, trees, and crosswalks.
Polylines: Autonomous vehicle technology can be taught to stay in the right lane without turning by annotating polylines on the road lanes and sidewalks.

What are the 6 types of data annotation?

6 types of data annotation are:

Polygon segmentation
Bounding boxes
3D cuboid
Text classification
Landmark annotation
Semantic segmentation

Klatch Technologies caters to all kinds of annotation or data labeling requirements for any project size.

What are benefits of image annotation?

The benefits of image annotation are:

Autonomous transportation and technology: Klatch’s image labeling helps build datasets for teaching self-driving software to recognize road signs, bike lanes, pedestrians, traffic lights, objects in the setting that could be dangerous, and weather conditions.
Agriculture: Large farmers and agriculture companies use Klatch’s image annotation services to protect their crops from damage. Computer vision in agriculture helps keep an eye on crop health, detect weeds and pests, manage livestock and do geo-sensing.
Security and surveillance: Klatch’s image annotation is used by companies that make security cameras and video tools to create training datasets for crowd detection, thermal vision, traffic motion, theft detection, and pedestrian tracking.
Medical AI: Klatch provides medical image data to train ML models for pharmaceuticals, medical devices, and health insurance companies with HIPAA-compliant image annotation.
Retail and E-Commerce: Katch delivers image labeling and computer vision solutions to retail and e-commerce brands to improve customer service, market research, inventory management, trend forecasting, and brand reputation.
Financial services: Klatch prepares datasets for ML models and technology for insurance and financial services firms. The models help improve customer satisfaction and reduce policy claim time and risk assessment.
Manufacturing: Klatch’s image annotation allows manufacturing companies to recognize robot productivity and maximize production efficiency. Intelligent robots are used to aid in the detection of faulty products or defects in manufacturing.

Is outsourcing image annotation projects safe?

We assure your data protection by combining streamlined image annotation operations and cross-quality checks. The results are supplied in a secure manner tailored to your specific requirements. Our data annotation teams adhere to a stringent nondisclosure agreement.

How do you annotate image?

Klatch has a specialized staff of qualified data annotators and subject matter experts who use a tried-and-true combination of manual operations. Our team of annotators begins by comprehending the project specifications as laid forth by the client. As a result, they conform to the current annotation dashboard and terminology. To enhance the utility of training datasets, they use a customized technique to extract semantic information from photos and add suitable labels and metadata. What seperates us from the competition is that our founders handle your projects and give direction on formatting data.

What is image annotation tool?

Image annotation tools are open-source, freeware, or paid tools available for annotating images. Some image annotation tools are CVAT, labelme, VoTT, imglab, and labelimg. Image annotation tools help with the labeling process by letting you draw complex shapes on an image and giving you a structured labeling system so you can label pictures correctly.

Klatch annotation specialists are proficient is all major image annotation tools and can perform using the tools as required by the client.

What are the 5 types of annotation?

5 types of annotation are:

Text annotation
Image annotation
Video annotation
Audio annotation
Semantic segmentation

Klatch Technologies caters to all kinds of annotation or data labeling requirements for any project size.

What are uses of image annotation?

The uses of image annotation are:

Object of interest detection: Object recognition is a crucial use in image annotation. In this step, the object is identified and labeled. An image annotation method is used to annotate these objects and make them detectable using computer vision.
Recognizing types of objects: It is essential to recognize what type of objects are identified. The diverse objects identified could be cars, bikers, street poles, sidewalks, pedestrians, trees, and buildings, as visible in the natural setting.
Objects classification: Models for machine-learning training entail that objects be classified. Image annotation utilizes several distinct methods to help an AI model’s detection and categorization of objects.
Semantic object segmentation: Segmentation image annotation assigns each image pixel to a class. Image annotation allows semantic segmentation and helps segment items by category, placement, and attributes in a single class.
Recognizing human faces: In image annotation, people’s faces are annotated from one point to another, gauging the dimension of the face and its various facial features to feed facial recognition algorithms.

What location are you in?

Our corporate headquarters are in India. Due to time zone variations, we have various offshoring centers in Asia and Africa. Our client support teams are available 24 hours a day, 7 days a week, and keep them up to date on the project’s status.