Undoubtedly, the introduction of machine learning and artificial intelligence has ushered in a revolution in various industries worldwide. These technologies have resulted in applications and devices far smarter than our wildest dreams.
The number of things that machines can be taught to perform is astounding – from speech recognition to navigation to even chess!
However, software engineers and developers have a lot of time and effort to train them to recognize patterns and correlations between variables to execute these astounding feats. It’s what machine learning is all about.
Computers are supplied massive amounts of data for training, validation, and testing. On the other hand, machine learning requires that these data sets be filtered and labeled to make the information more understandable. Data annotation is a term used to describe a procedure.
Table of Contents
What is data annotation?
Data annotation is a method of labeling data found in various media, including photographs, texts, and videos. The data is labeled to make items recognizable to computer vision, which helps the machine learn more. In a nutshell, the procedure aids the machine in comprehending and remembering the input patterns.
Types of Data Annotation:
Various AI data annotation methods are available to build the data collection required for machine learning. The primary goal of all of these annotations is to aid in recognition of text, photos, and videos (objects) by a machine using computer vision.
Audio annotation is a division of data annotation that entails classifying audio components from people, animals, the environment, instruments, and other sources. Engineers employ data formats including MP3, FLAC, and AAC for the annotating process. Like all other annotation types (such as image and text annotation), audio annotation necessitates physical labor and annotation software designed specifically for the task.
Text annotation helps machines understand the text better. Chatbots, for example, may recognize users’ queries using keywords taught to the machine and provide solutions. The machine is unlikely to offer a helpful answer if the annotations are incorrect. A better customer experience is provided through improved text annotations. Text annotation gives specific keywords, sentences, and other items to data points during the data annotation process.
This sort of annotation uses bounding boxes (imaginary boxes painted on an image) and semantic segmentation to ensure that machines recognize an annotated area as a separate object (the assignment of meaning to every pixel). Such labeled datasets can be used to guide self-driving cars or in facial recognition algorithms.
For example, with tagged digital photos, a machine learning model gets a high level of comprehension and can comprehend the images it sees. Objects in any image can be labeled with data annotation. The increase in the number of image labels depends on the use case.
Video annotation, like picture annotation, uses techniques like bounding boxes to recognize movement on a frame-by-frame basis or via a video annotation tool. As a result, computer vision algorithms that do localization and object tracking rely heavily on data obtained by video annotation.