Data Annotation Services

In machine learning, data annotation is the process of labeling data in various formats, such as texts, videos, and images. The labels are added as tags and metatags, allowing natural language processing (NLP) and computer vision-based models to calculate attributes properly.

5 common data annotation examples are:

• Bounding Boxes: Development of self-driving cars are an data annotation example. Klatch annotators marked vehicles, bikes, and people in traffic. The annotated objects 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 data.
• 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 trucks.
• Semantic segmentation: Klatch annotators are provided datasets to label each 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.

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.

Data annotation is needed or benfits in:

• 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.

We assure your data protection by combining streamlined data 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.

Annotating data can be done in-house until there is too much data to handle. When things get hard, you’ll need specialists and the correct tools to label them accurately. For example, if you are developing a medical product, you will need a subject matter expert to classify objects found on an MRI report. Klatch data specialists can annotate datasets in various processes.

Klatch has a specialized staff of qualified data annotators and subject matter experts who use a tried-and-true combination of manual operations to label data. 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 datasets and add suitable labels and attributes. What seperates us from the competition is that our founders handle your projects and give direction on formatting data.

Guided training is essential for machine learning algorithms to make sense of large datasets. As a result, consistent access to large, high-quality, accurate, and relevant datasets is a must for data annotation. It is done to guarantee that the results are credible and can make confident decisions.

Klatch Technologies offers data collection services to prepare and provide various relevant, high-quality, and unique datasets.

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.

The uses of data annotation are:

• Object of interest detection: Object recognition is a crucial use in data annotation. In this step, the object is identified and labeled. A data 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. Data annotation utilizes several distinct methods to help an AI model’s detection and categorization of objects.
• Semantic object segmentation: Segmentation annotation assigns each data pixel to a class. Data annotation allows semantic segmentation and helps segment items by category, placement, and attributes in a single class.
• Recognizing human faces: In data 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.

Our corporate headquarters are in India. 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.