What is annotation and why is it important for AI/ML models?

What is Data Annotation?  

Data annotation is typically the method of labeling different contents identifiable to machines. The identification takes place with the help of computer vision or Natural Language Processing (NLP). To put it simply, data annotation adds Metadata to the datasets. This could be in the form of tags, filters, images, videos etc.  

With data annotation, an object of interest is easily identifiable. For instance, when a particular algorithm successfully processes sufficient annotated datasets, it keeps following the same pattern repeatedly. Data annotation can of different types and the one you select is purely based on your project requirements.  

What are the types of data annotation?  

Here are the most used types of data annotation:  

1. Image annotation – image annotation is the process of labeling different objects within a particular image. It can be of different formats where each pixel in an image s associated with a meaning. With the application of image annotation, an AI/ML model can easily identify the annotated region as a distinct object.   

2. Video annotation – video annotation is much similar to text annotation. To perform this type of annotation, one can add polygons, bounding boxes to detect particular objects. Video annotation works on a frame by frame basis to track the movement of an annotated object.  

3. Text annotation – text annotation is the simple process of assigning some pre-defined segments to a particular document. For instance, one may label sentences and paragraphs in a document based on topics.  

4. Entity annotation – entity annotation is an interesting annotation feature where unstructured phrases and sentences are processed to make them readable for the machine. Entity annotation is further of two types, namely named entity recognition and entity linking.  

In this case, any content is labeled depending on pre-defined categories, be it persons, places, or companies. On the contrary, entity linking is used for annotating the interrelationship between two different parts of a text.  

Why is Annotation important for AI/ML models? 

Annotation can do much more than you expect. Here’s why you must opt for an annotation to train your AI/ML models:  

1. Identify any object of interest within an image   

Any AI/ML models need to be trained enough to detect different objects in an image. Take the example of an autonomous vehicle. How do you think it can function unless it is trained to detect objects? With annotation, the detection of an object of interest becomes pretty easy.  

To be more specific, bounding box image annotation helps recognize different machines using computer vision. This image annotation technique is good enough to train AI/ML models for different sectors.  

2. Detect different image classes  

A single image consists of different classes of objects. What if all these object classes are of equal dimension? The result will be a great mess as classifying different objects will be difficult in that case.  

The technique of semantic segmentation helps in classifying all single class objects that fall under the same category. Hence, differentiating objects from one another is simpler. In the case of computer vision, one can configure the image objects with a nested classification method.  

3. Arrange ML trainings  

Image annotation is also important because it trains different ML models to label datasets. If you’re planning for a supervised ML model, you can never neglect the importance of annotation as it adds more detectable features to algorithms.  

Final Words  

Our AI/ML tool Artificio can help you out with image annotation. We’re currently offering 3 types of annotation viz. image annotation, text annotation, and document annotation. Artificio is flexible, scalable, secured and affordable. Try Artificio for a seamless annotation experience.