Data Annotation: Everything You Need to Know

What is Data Annotation?

For a while now, machine learning and artificial intelligence have been the talk of the IT community. But what goes into creating these technologies? What influences how the AI model interprets and forecasts the result?

The answer is straightforward: massive amounts of training data are necessary for AI and ML that behave like humans. In the same way, a child is taught to be a better person, the machine needs to be trained to interpret specific information in order to carry out the proper function and make corrective decisions.

Thus, data annotation is the labelling and categorising of various types of data for AI applications. For the AI model to function properly, training data must be correctly categorised and annotated. Expertise in categorising and classifying data, whether it be in text, video, image, or audio format, is required for data annotation. The quality of the annotation will improve the final product.
The key to hacking AI is annotation. It offers the foundational data for AI. From sequencing to categorization to segmentation to mapping, the appropriate data is the key, and human annotators are the way to let it into the AI model. A better client experience is the final result, with features like chatbots, speech recognition, predictive gaming, and recommendation engines.


Types of Data Annotation:


Image Annotation

Image annotation is the process of identifying or categorising a picture using text, annotation tools, or both to exhibit the data properties you anticipate your system to recognise on its own. While the quantity and variety of your image data are undoubtedly growing every day, getting photos annotated to your standards might be a challenge that delays your project and, as a result, your time to market. You should give serious thought to the decisions you make about the staff, tools, and procedures for picture annotation.

Types of Image Annotation:

  • Semantic Segmentation
  • Tagging
  • Bounding Boxes
  • Polygon Annotation
  • 3D Bounding Boxes
  • 2D Bounding Boxes
  • Lane Annotation
  • Landmarks
  • Skeletal Annotation
  • Image Masking


Video Annotation

Video annotation is the procedure of labelling or tagging video clips that are used to train computer vision algorithms to detect or identify objects. Contrary to image annotation, frame-by-frame annotation in video annotation identifies items for machine learning models.

For the best machine learning performance, high-quality video annotation produces ground truth datasets. Numerous deep learning applications exist for video annotation in a variety of fields, including self-driving cars, medical AI, and geospatial technology.

Types of Video Annotation:

  • Semantic Segmentation
  • Instance Segmentation
  • Polygon Annotation
  • 3D Bounding Boxes
  • 2D Bounding Boxes
  • Lane Annotation
  • Landmarks / KeyPoint Annotation
  • Skeletal Annotation


Text Annotation

Text annotation is the process of recognising and tagging phrases with additional details or metadata to characterise their properties. Depending on the scope of a project, this information may highlight certain sentences’ parts of speech, grammar and syntax, keywords and phrases, emotions, sarcasm, feelings, and more. In order to help machine learning modules better understand human interactions, such AI training data is supplied to them. Here, the modules learn various parts of sentences, sentence construction, and more. They get better at imitating human speech as they gain knowledge from correctly labelled data (current virtual assistants). But if you give them data that isn’t well annotated, they’ll give you answers that are either irrelevant, stupid, or deceptive.

Types of Text Annotation:

  • Entity Annotation
  • Entity Linking
  • Linguistic Annotation
  • Text Classification


Audio Annotation

It appears that categorising audio elements from people, animals, the environment, instruments, and other sources falls within the category of audio annotation, a subclass of data annotation. For the annotation process, engineers use a variety of data types, including MP3, FLAC, and AAC. Making sounds understandable to machines is achieved through the use of audio annotation. Deep learning is being used to train robots to understand audio or voice input in any format. In order to improve the understanding of sounds by apps like chatbots or virtual assistant computers, NLP-based speech recognition models include annotated audio commentaries.

Types of Audio Annotation:

  • Text Transcription
  • Intent & Conversation Analysis
  • Sentiment & Topic Analysis
  • Named Entity Recognition (NER) & Entity Classification


Lidar Annotation

LIDAR, short for Light Detection and Ranging, is a kind of remote sensing technology that employs light to examine a surface and its components. When detecting objects, LIDAR uses pulsed lasers as opposed to radar, which uses electromagnetic pulses. It may be utilised in a wide range of industries, including meteorology, mining, planning for infrastructure, archaeology, and environmental monitoring due to its adaptability and high resolution.

Types of Lidar Annotation:

  • Bounding Box Annotation
  • Polyline Annotation
  • Polygon Annotation
  • 3D Point Cloud Annotation
  • 3D Cuboid Annotation
  • Landmark Annotation

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