How to deal with best data labeling in machine learning?

The Purpose of Data Labeling: Machine Learning

Machine learning is embedded in artificial intelligence, allowing machines to be trained to perform specific tasks. With data annotation, it can learn about almost everything. Machine learning techniques can be described as four types: unsupervised learning, semi-supervised learning, supervised learning, and reinforcement learning

▸ Supervised learning: Supervised learning learns from a set of labeled data. It is an algorithm that predicts outcomes for new data based on previously known labeled data.

▸ Unsupervised Learning: In unsupervised machine learning, training is based on unlabeled data. In this algorithm, you do not know the outcome or labels of the input data.

▸ Semi-supervised learning: AI will learn from partially labeled datasets. This is a combination of the two types above.

▸ Reinforcement Learning: Reinforcement Learning are algorithms that help a system determine its behavior to maximize payoff. Currently, it is mostly used in game theory, where an algorithm needs to determine the next move to achieve the highest score.

While there are four types of techniques, the most commonly used are unsupervised learning and supervised learning.

What is labeled data?

Labeled data is a set of samples with one or more labels. Labeling typically takes an unlabeled set of data and augments each part of it with informative labels. Labeled data will help machine learning “learn” similar patterns in the input data and then predict another data set.

How to handle data labeling?

Step 1: Data Collection

Data collection is the process of gathering and measuring information from a myriad of different sources. To use the data we collect to develop useful artificial intelligence (AI) and machine learning solutions, it must be collected and stored in a way that makes sense for the business problem at hand.

There are several ways to find data. In the case of classification algorithms, keywords can be formed by relying on class names, and images can be found by crawling data from the Internet. Or you can find photos, videos from social networking sites, satellite images on Google, data collected for free from public cameras or cars, or even buy data from third parties (be aware of the accuracy of the data). Some common datasets can be found on free websites, some common data types are image, video, text, audio, and 3D sensor data.

①Images  ( photographs of people, objects, or animals, etc.)

Images are probably the most common data type in the field of data annotation . Since it deals with the most basic types of data, it plays an important role in a wide range of applications, be it robotic vision, facial recognition, or any application where images must be interpreted.

From raw data sets provided by multiple sources, it is crucial to tag these data with metadata including identifiers, titles or keywords.

The main areas that require significant effort for data labeling are healthcare applications (such as our blood cell labeling case study), autonomous vehicles (such as our traffic light and sign labeling case study). By effectively and accurately annotating images, AI applications can function flawlessly without human intervention.

In order to train these solutions, metadata must be assigned to images in the form of identifiers, titles or keywords. From computer vision systems used by self-driving vehicles and machines that pick and sort products, to healthcare applications that automatically identify medical conditions, there are many use cases that require large numbers of annotated images. Image annotation improves precision and accuracy by efficiently training these systems.

Video (closed-circuit television or videotape recorded by a video camera, usually divided into scenes)

Compared to images, videos are a more complex form of data and require greater effort to be properly annotated. Simply put, a video consists of different frames, which can be understood as pictures. For example, a one-minute video can have thousands of frames, and it takes a lot of time to label this video.

A prominent feature of video annotation in artificial intelligence and machine learning models is that it provides deep insights into how objects move and their orientation.

Video can also tell if an object is partially occluded, whereas image annotation is limited to that.

②Text  : Different types of documents include numbers and words, which can be in multiple languages .

Algorithms use large amounts of labeled data to train AI models as part of a larger data labeling workflow. In the labeling process, metadata tags are used to mark the characteristics of the dataset. With text annotation, the data includes labels that highlight criteria such as keywords, phrases, or sentences. In some applications, text annotation can also include labeling various emotions in the text, such as “anger” or “sarcasm,” to teach machines how to recognize the human intent or emotion behind the words.

The labeled data (called training data) is the data that the machine processes. Target? Help machines understand human natural language. This process, combined with data preprocessing and labeling, is called Natural Language Processing or NLP.

③Audio  : These are recordings of voices from people with different demographic characteristics.

With the development trend of the voice AI data annotation market, LTS provides first-class services in voice data annotation. We have annotators who are proficient in the language.

All types of sounds recorded as audio files can be annotated with additional themes and suitable metadata. The speech in the audio file contains different words and sentences aimed at the listener. When annotating audio, it is possible to make such phrases in audio files machine-recognizable using special data-tagging techniques. In NLP or NLU, machine algorithms for speech recognition require audio language annotations to recognize such audio.

3D sensor data : 3D models generated by sensor devices.

Regardless, money is always a factor. 3D-capable sensors vary widely in build complexity and therefore can range in price from hundreds to thousands of dollars. Opting for them over a standard camera setup isn’t cheap, especially considering you’ll often need multiple units to guarantee a large enough field of view.

low resolution data

In many cases, the data collected by 3D sensors is nowhere near as data-intensive or high-resolution as traditional cameras. In the case of lidar, standard sensors discretize the vertical space into rows (the number of rows varies), each sensor has hundreds of detection points. This yields approximately 1000 times fewer data points than would be contained in a standard high-definition picture. Furthermore, due to the conical shape of the laser beam propagation, the farther away the object is, the less sample falls on it. Therefore, the difficulty of detecting objects grows exponentially with their distance from the sensor. ”

Step 2: Identify the problem

Knowing the problem you’re dealing with will help you decide which technique you should use on your input data. In computer vision, there are tasks such as:

Image Classification: Collect and classify input data by assigning class labels to images.

Object detection and localization: Detect and localize the presence of objects in images and indicate their locations with bounding boxes, points, lines, or polylines.

– Object Instance/Semantic Segmentation: In semantic segmentation, you have to label each pixel with a class of objects (car, person, dog, etc.) and non-objects (water, sky, road, etc.). Polygon and masking tools are available for object semantic segmentation.

Step 3: Data labeling

With the problem identified, you can now handle data labeling accordingly. For classification tasks, tags are keywords used during finding and scraping data. For segmentation tasks, for example, each pixel of an image should have a label. After obtaining the tags, you need to use tools for image annotation (that is, to set tags and metadata for the images).

Types of data annotations

Data annotation is the process of labeling a training dataset, which can be images, video, or audio. Needless to say, AI annotations are critical to machine learning (ML), as ML algorithms require (quality) labeled data to process.

In our AI training projects, we use different types of annotations. Which type you choose to use depends largely on which data and labeling tools you are working with.

Polygons: For more precise results when it comes to irregular shapes like human bodies, signs, or street signs, polygons should be your choice. Boundaries drawn around objects provide an accurate understanding of shape and size, which can help machines make better predictions.

Polylines : Polylines are often used as a solution to reduce the weakness of bounding boxes, which often contain unnecessary space. Mainly used for lane labeling on road images.

3D Cuboid: A 3D cuboid is used to measure the volume of an object, be it a vehicle, building or furniture.

Segmentation: Segmentation is similar to polygons, but more complex. While polygons just select some objects of interest, with segmentation, layers of similar objects are labeled until every pixel of the picture is complete, which leads to better detection results.

Landmarks: Landmark annotation can be used for face and emotion recognition, human pose estimation, and body detection. Applications using data labeled by landmarks can indicate the density of objects of interest in a particular scene.

Popular data annotation tools

In machine learning, data processing and analysis are extremely important, so I will introduce some tools for labeling data to make the job easier.

You can refer more information about data labeling here

Data Generator Tool

Text Recognition Data Generator is a tool for generating text.

Using this tool, you can generate different fonts and colors for text detection problems.

Who can annotate data?

A data annotator is someone who is responsible for labeling data. There are a few ways to assign them:


Data scientists and AI researchers on your team label the data. The advantage of this method is that it is easy to manage and has high accuracy. However, this is a waste of human resources, as data scientists will have to spend a lot of time and effort on manual, repetitive tasks.


You can find third parties—companies that provide data annotation services. While this option will reduce your team’s time and effort, you need to ensure that your company is committed to providing transparent and accurate data.