Image annotation is the process of tagging objects in an image with different labels to give it as data to the programs using neural network algorithms in order to enable the programs to ‘see’ and understand things like humans do.


Computer vision is a field of artificial intelligence, and image annotation is one of the important tasks in computer vision. With computer vision, we look to emulate vision like a human’s in machines. To look at a scene and understand what it is. We learned this from our experiences, the machines are also taught in the same way. To do this we need training data, lots of it.

We create these training data with image annotations. One of the basic tasks of every computer vision task is image annotation. Image annotation plays a huge role in making facial recognition, autonomous vehicles and many other computer vision technologies possible.


Artificial Intelligence and Computer Vision are highly advanced fields. But to build training data, it doesn’t ask for the same skills to make a self-driving car. However, to build these training data the annotators should properly understand the project specifications and guidelines. Every company may have different requirements.

Image Annotation
Image Annotation

Above is an example of an image annotation. This contains pieces of information like, what are the objects in the image and where exactly they are in the image. The annotation should provide a boundary that follows the object outline as accurate as possible. Here it is labeled cars but the labels and category may vary according to the project variations and demands. Like, Some may projects may even ask the annotator to categorize the labels as cars, vans, trucks and so on. 

When the object has sharp edges it is easy to annotate. As more round or curved the edges become we’ll have to use more points to outline the object accurately. Then we’ll have to use different techniques to get the annotations done. There are many techniques used in addition to the most commonly used bounding boxes. The project demands sometimes include the change of techniques of image annotation also.

Different techniques in image annotations

Bounding boxes

One of the simplest techniques in image annotation. Not desired when building a high precision CV model.

Semantic segmentation

It is one of the most accurate techniques of image annotation. Here the objects are annotated at its pixel levels. Creating training data with semantic technique is a more time-consuming process than with bounding boxes. But one can build a much greater precise machine with these training data.


This technique is found as a favorite in medical and sports projects.

Cuboid/3D annotations

This technique is used in both 2D images and 3D point cloud data. It is a little more complicated method than bounding boxes to do annotations.


This is one of the common methods of annotations when producing data for lane detection of autonomous vehicles.

Whatever be the techniques used in annotation the reason why it is done is the same. It reduces the search area of CV models for a particular object or thing and contains information about where it exactly resides in the image.

For more on different techniques in image annotation check here


Annotators label images according to the directions given. A platform is needed to annotate all these images. There are open-source tools to do these annotations in but they haven’t always succeeded in meeting everyone’s project constraints. And the companies providing image annotation services have developed tools of their own to meet project needs. In order to fulfill the project demands companies like Infolks even customizes their tools.

Steps in image annotation

1) Project constraints are analyzed.

Before starting any annotation work, it is essential that the annotators are given an idea about the project’s demands and restrictions.

2)An appropriate tool to do the image annotation is selected.

There are many tools available to do image annotations. But not every tool can get the work done as the projects need it do be done. One important step in image annotation is selecting the right tool for the work.

3) The objects in the images are annotated using appropriate techniques.

The objects in images are annotated as per the project instructions and sometimes the project demands the annotator to a particular technique.

The images produced this way serve as data to train the computer vision programs and hence called training data. These training data plays a major role in how your programs behave, so selecting experienced annotators or crowdsourcing companies is a crucial task too.

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