How is Text annotation different from image annotation?
The application of humongous data sets available via ever-expanding text, digital images, videos, and deep learning models make it possible to train computers to interpret and understand the visual world in ways that are similar to how humans do. Deep learning and machine learning are changing the way we interact with technology because of the advancements in deep learning that allows machines to understand natural language with a high degree of accuracy.
Creating large data sets with the proper context; is where data annotation and labeling step in. The computer vision model’s accuracy is determined by the quality of these annotations and labeling, which are used beyond the classification of objects. The annotation process includes providing feedback to the machine learning algorithm that trains it so that the system can improve its prediction capabilities.
Broad understanding of why text annotation or image annotation is used.
Both text and image annotations are used to enrich the information on a given topic or data set. This allows for an interactive experience and provides the viewer with additional details.
With text and image annotations, one can discern important information such as the location of a specific landmark in a photograph, highlight key moments in a video or find the intent of a public release.
Technology can be used as a platform for the efficient processing of information in different ways, such as through text annotation or image annotation. When text annotation or image annotation is done on public data sets with AI/ML models, feedback also helps improve AI/ML models.
In short, both text annotation and image annotation, along with video annotation, are a subset of data annotation and labelling and are used in improving the context, intent, and sentiment of information (which can be text, image or a video) with the help of AI/ML tools.
Text Annotation Explained
Text annotation is a process where text is annotated with tags or other metadata. An annotation might look like highlighting information, marking a text with symbols to represent ideas, or writing summaries at the end of a chapter or section for an easy review.
The annotation can be done automatically by using optical character recognition (OCR) and natural language processing (NLP) or manually by a trained human.
Text annotations can be used to describe the content of documents, categorize them or find specific information in them.
Annotation tools are often used in libraries and archives to help organize and find resources. They are also used in the publishing industry, providing valuable information about the publication of books, journals and articles.
In addition, annotation tools may help mark-up web pages with metadata such as keywords and page rank.
Statista shows that the global NLP market will generate revenues of over $12 billion in 2023-24. The market is predicted to grow by 25% per year, which will result in revenues of over $43 billion by 2025. Text annotations are an essential part of the NLP development procedure. It is fair to consider text annotations as an important phenomenon.
Types: Text annotation can be of different types based on different data set and use case.
a. Entity Annotation & Entity Linking: Entity annotation is one of the most critical tasks in NL processes. It is essentially the process of locating, extracting and tagging entities in text. On the other hand, entity linking involves connecting your entities to more extensive data repositories.
b. Text Classification: Text classification tasks involve annotators reading through a body of text and classifying its overarching subject, intent, and sentiment. Text classification can be used for various reasons, such as telling the difference between when food allergies are mentioned versus allergies to specific things unrelated to food. Text classification is also known as document classification and text categorization.
c. Sentiment Annotation: More broadly called sentiment analysis or opinion mining, sentiment annotation is the process of labelling emotion, opinion, or sentiment within a piece of text.
d. Linguistic Annotation: With linguistic annotation, annotators are either identifying or flagging grammatical, semantic or phonetic pieces of text. Linguistic annotation is a crucial component of the more general label text analysis, which includes annotating other textual features such as punctuation, tone and word length. Annotating linguistic text includes identifying words or phrases that have unclear meaning due to the use of cultural, regional or other language-specific words.
Image Annotation Explained
Image Annotation is the process of adding labels to images. In a simple case, it’s like giving human annotators images of different cars and asking them to label each image with its colour, model name and country of origin.
Annotation can be done manually or with the help of machine learning. The accuracy of image annotation is improved by using a model trained on annotated images from a large data set. The training process involves labelling different parts of an image and assigning them semantic labels.
With the help of image annotation, it is possible to scale up workflows for image processing.
Computer vision (a field of AI, ML) continues to be an ever-increasing part of our lives. It’s gone from healthcare, automotive and marketing to even online dating. According to Forbes, the computer vision market is predicted to be worth $50 billion in 2022.
Types: Image annotation can be of different types based on what we use it on.
a. Bounding Boxes: A frame is drawn around the object to be identified. You can draw a bounding box around 2D or 3D images.
b. Landmarking: It is a technique that identifies facial features, gestures, expressions and emotions. It’s also used for marking body position & orientation.
c. Masking: These are pixel-level annotations that hide some portion of an image and make other areas more visible. The primary use for this is to focus on specific parts of the image or highlight what you want people to notice.
d. Polygon: This technique is used to identify the pick point of the object in question and its edges: Useful case of polygon labelling is to identify objects and items with irregular shapes.
e. Polyline: This model helps create ML models for computer vision and autonomous vehicles, and it helps to recognize directions, signs, turns, and incoming stream of vehicles to make sense of their environment and be safe when driving.
The Advantages of Text Annotations over Image Annotations
Text annotations are more versatile than image annotations. Text annotation can be used in many different formats, and they are searchable. Images are not searchable and don’t have the same level of versatility as text annotations.
Text annotations are a form of visual representation of the text on a platform. They can be created by highlighting, underlining, circling or adding comments, and it is up to the user to decide what information gets displayed in these annotations. Some advantages of text annotations include making better reading comprehension and highlighting keywords.
The Disadvantages of Text Annotations over Image Annotations
Text annotations are often used in educational settings to provide additional information to the reader. However, text annotations have some disadvantages that make them less desirable than image annotations.
Text Annotations: Text annotations can be challenging to create and maintain, especially if they need to be updated regularly. They also require more screen space than image annotations, making it difficult for students with visual impairments or learning disabilities to use them.
Image Annotations: They are easier for students with visual impairments or learning disabilities because they don’t require as much screen space and can be created and maintained more easily.
Text annotation is a relatively new research field still in its infancy. It is very different from image annotation, which has been researched for decades. Image recognition accuracy only recently improved, making image annotation more relevant in the past ~10 years.
Image annotation is a difficult task for humans and robots alike. It requires understanding the image’s content and then providing labels to help describe it. Text annotation, on the other hand, is more linguistically oriented. Its goal is to understand the content of a document, extract facts from it, and provide contextual information such as definitions or synonyms.
Text annotation uses natural language processing to annotate text with metadata information such as sentiment, topic, and other relevant information.
Image annotation can be done by hand, or it can be automated using computer vision techniques.
Most likely, in the coming time, text annotations will be used to annotate images and videos as well. We are already seeing promising results in AI-generated artworks where we can generate images by adding a few descriptive words as seed phrases.
Vendor of choice for your project
Ekit.AI provides the text annotation, text labelling & image annotation services for NLP in deep learning. We work with highly experienced annotators to identify such texts and label or annotate them per the custom requirements. High quality and the accuracy is maintained to make sure each vital text/image is annotated with suitable metadata to make sure it can produce the correct mix of labelling and annotation for different needs at the best pricing in the market.