Data Annotator

Data types for markup

Image markup

Our specialists work with visual materials to create precise designations of objects and areas. Typical tasks include:

• selecting objects using frames and contours;
• detection of specific objects and elements in an image;
• segmentation, that is, dividing an image into significant areas;
• identification of key points and landmarks for the analysis of poses and forms.

Text markup

Texts are systematized for subsequent use in teaching information processing algorithms. Key areas of work:

• identifying the author's intentions and the purpose of the messages;
• analysis of the emotional coloring of the text;
• recognition of entities such as names, companies, dates and addresses;
• classification of materials by subject or content type.

Annotating audio

Audio files are converted into organized information for training sound recognition and analysis systems . The process includes:

• speech-to-text transcription;
• speaker marking and voice separation;
• classification of sounds and audio fragments into categories.

Annotating videos

Video is used to prepare systems that track objects and events in motion. Key processes:

• tracking the movement of objects in frames;
• action recognition;
• recording and analysis of events occurring in the frame.

Data markup tools

Our specialists work with professional platforms for labeling and structuring information. These specialized tools enable the preparation of high-quality datasets for machine learning models and artificial intelligence systems.

Examples of tools:

1. Label Studio is a universal system for annotating images, text, audio and video.
2. CVAT is a tool for detailed video and image annotation with team collaboration capabilities.
3. Supervisely is a platform for comprehensive annotation, analysis, and management of large datasets.
4. Doccano is a specialized system for text systematization, classification and entity recognition.
5. Labelbox is a platform for organizing the process of data labeling, quality control, and preparation of training sets.

Data labeling process

Data labeling is an important step in preparing training data for machine learning models and artificial intelligence systems. The effectiveness of the algorithms and the project's success depend on the structure and reliability of the training data.

Data preparation

At this stage, all necessary materials for layout are collected and processed: images, text, audio, and video files. Specialists check the integrity of the information, remove duplicates, incorrect, or damaged files, and organize the data for ease of further work.

Creating markup rules

Before the start of the markup process, clear instructions and standards are developed for specialists. This ensures that all data is processed consistently and in accordance with project requirements, regardless of who is performing the markup.

Data markup

Specialists perform the actual labeling of objects, identifying entities in texts, annotating audio and video materials, classifying, and tagging. All work is performed in accordance with established rules to ensure the orderliness and accuracy of datasets.

Quality control

Particular attention is paid to checking the accuracy and consistency of the markup. Each file undergoes additional analysis, and inconsistencies are corrected to avoid errors during model training. Quality control is a key step in determining the effectiveness of AI systems.

Dataset validation

Once the labeling is complete, the entire dataset is checked for compliance with the project requirements. The completeness, correctness, and logical consistency of the data are tested to ensure the training materials are ready for use in the algorithms.

Transfer of the training kit

At the final stage, the completed, systematically organized dataset is handed over to the client. It can be used for training models, testing algorithms , and further optimizing machine learning and artificial intelligence systems.

Applying data markup

Computer vision

Image and video labeling is used to train systems that analyze visual information , such as:

• autonomous vehicles – recognition of road objects, pedestrians, signs and traffic lights to ensure safe movement of vehicles;
• production quality control – identification of defects on products, such as cracks, scratches or deformations;
• Object recognition – training systems to identify objects, people, or important details in images.

Natural Language Processing (NLP)

Text markup helps algorithms understand the content of messages and documents. Examples of application:

• Chatbots – preparing messages to train automatic response systems ;
• review analysis – determining user sentiment, evaluating positive, negative or neutral reviews;
• Document classification – automatic sorting of materials by category and purpose.

Audio and voice systems

• Voice assistants – preparation of training files to detect user commands;
• Speech recognition – creating reliable transcriptions for automatic audio-to-text conversion .

Where is data markup used in practice?

1. Object detection for autonomous driving

Road object marking in images and videos for training autonomous driving systems.

Markup type:

• selection with frames and contours;
• segmentation.

Objects:

• cars;
• pedestrians;
• road signs;
• traffic lights.

2. Quality control in production

Marking defects in product photographs for training automatic quality control systems.

Markup type:

• defect detection;
• segmentation.

Examples:

• cracks;
• scratches;
• deformations.

3. Labeling of medical images

Preparing images for training diagnostic systems.

Data type:

• MRI;
• CT;
• X-ray.

Tasks:

• tumor detection;
• pathology analysis;
• segmentation of organs.

4. Classification of texts for customer support

Markup of customer messages for training request processing systems.

Markup type:

• determination of intentions;
• sentiment analysis.

Examples of categories:

• return of goods;
• complaint;
• technical problem;
• request for information.

5. Recognizing entities in documents

Entity tagging in texts for automatic analysis.

Markup type: entity extraction.

Examples:

• people's names;
• company names;
• addresses;
• dates;
• amounts.

6. Sentiment analysis of social media posts

Tagging posts and comments to study user attitudes toward brands and products.

Markup type:

• positive;
• neutral;
• negative.

Used for:

• marketing analytics;
• reputation monitoring.

7. Audio transcription for voice assistants

Processing audio materials for training speech recognition systems .

Markup type:

• speech to text conversion;
• speaker marking.

Used for:

• voice assistants;
• automated call centers.

8. Video markup for CCTV systems

Extracting objects and events from video for training surveillance systems.

Markup type:

• object tracking;
• action recognition.

Examples of events:

• movement of people;
• suspicious activity;
• violation of the rules.

9. Product recognition for e-commerce

Labeling product images for training automatic classification systems.

Markup type:

• classification of objects;
• assigning labels.

Used for:

• automatic categorization of goods;
• visual search.

10. Preparing data for recommendation systems

User action labeling for training recommendation algorithms.

Markup type:

• user behavior labeling;
• Relevance assessment.

Examples:

• clicks;
• purchases;
• interests of users.

Used for:

• personalized recommendations;
• audience behavior analysis.

11. Satellite image tagging for land monitoring

Labeling images from orbital satellites to analyze the condition of agricultural land, forests, and water bodies.

Markup type:

• segmentation;
• classification of objects.

Examples:

• fields and crops;
• forest areas;
• reservoirs.

Used for:

• monitoring the condition of lands;
• crop yield forecasting;
• environmental control.

12. Annotation of industrial drawings and diagrams

Marking up technical drawings and diagrams for automatic control of production processes.

Markup type:

• selection of objects and nodes;
• marking of errors and defects.

Examples:

• pipelines;
• mechanical parts;
• electrical circuits.

Used for:

• production quality control;
• automation of processes;
• identifying deviations and errors.

13. Preparing data for robotics

Labeling sensory information and images to train robots to navigate and interact with objects safely.

Markup type:

• segmentation;
• object tracking.

Examples:

• obstacles;
• routes of movement;
• interactive elements.

Used for:

• robot training;
• testing navigation algorithms;
• optimization of interaction with the environment.

14. Biometric data labeling

Processing and annotation of biometric data for identification and security systems.

Markup type:

• classification;
• highlighting key points.

Examples:

• faces;
• fingerprints;
• iris of the eye.

Used for:

• user identification;
• ensuring security;
• access control.

15. Smart Device Data Processing (IoT)

Labeling data from sensors and smart devices to predict equipment conditions and prevent accidents.

Markup type:

• event classification;
• identification of anomalies.

Examples:

• temperature and pressure sensor readings;
• motion and vibration signals;
• failure notifications.

Used for:

• predictive maintenance;
• monitoring of equipment operation;
• increasing the reliability of systems.

Why hire a data labeler at CortexIntellect?

Data labeling is a critical step in developing machine learning models, as the algorithms' performance directly depends on the quality of the training data. Working with our team ensures that your data is prepared with maximum accuracy and is ready for use in your AI project.

The main advantages of working with CortexIntellect:

• Experience in artificial intelligence projects – our machine learning engineers use pre-built datasets to train models and optimize algorithms, while our AI developers create and implement intelligent solutions, ensuring their stable operation;
• Preparing training sets for models – we structure the data in such a way that models can immediately use it for training.
• Markup quality control – we check the accuracy, consistency, and correctness of data at every stage.
• A flexible team of specialists – we select the optimal composition for specific tasks and work volumes.
• Work with various types of data – images, text, audio, video – everything you need for your models.

Contact us to select a team or specialist for your AI project.