Importance of Data Annotation in Supervised ML
Data Annotation
The process of labeling data is known as data annotation. However, having raw data is not sufficient because it is not enough to simply feed a computer enormous amounts of data and expect it to learn to talk.
Data must be collected and presented in such a way that the machine learning model can spot patterns and draw conclusions from them, like identifying the items in a given image, understanding human speech, and performing a variety of other functions.
Data annotation is everywhere, starting with speech recognition platforms, autonomous vehicles, and translation systems.
They all benefit from annotated data sets, and so will you once you finish reading this article!
Image Annotation
Annotating a text or a photo is simply assigning predetermined categories and tags to documents and images, which is used to improve search relevancy and help in the training of chatbots.
Image annotation is the process of labeling images in order to train an AI or machine learning model to perceive and interpret them in the same way that humans do. We can divide picture annotation into two categories: segmentation and classification.
Image segmentation facilitates image analysis, it separates the image into several segments known as image objects, and we can divide it into three types:
- Instance segmentation is the process of defining entity attributes like position and number.
- Semantic segmentation is the labeling of related items in an image based on features such as size and location.
- The combination of semantic and instance segmentation results in panoptic segmentation.
Classification of images uses predetermined annotated images to determine what an image represents, whereas detection of objects is a more advanced variant of image classification. It is an accurate depiction of the image’s numbers and places.
Text Annotation
During the data annotation process, text annotation assigns specific keywords, sentences, and so on to data points. To further clarify the concept, we will provide a couple of text annotation types:
- Sentiment annotation identifies emotions inside the text and helps machines recognize human emotions through words.
- Text categorization assigns categories based on the subject to phrases in a text or a paragraph, making it easier for users to find the information they seek.
- Semantic annotation is the process of tagging text documents with the relevant concepts, which makes it easier to find unstructured content.
- OCR Annotation is a text annotation tool with an OCR feature that is necessary to annotate text from digital and handwritten photographs in a precise layout.
For example, UBIAI OCR annotation features combine computer vision techniques and natural language processing and provide categorization, annotation, relation extraction, and NER on native PDFs, native scanned photos, photographs, invoices, receipts, and reports.
Audio & Video Annotation
Audio annotation aids in the development and improvement of voice-enabled applications by analyzing and categorizing audio clips, whereas video annotation is the addition of metadata to an unlabeled video that can train a machine learning model for a variety of tasks, ranging from simple classification to object tracking across multiple frames.
The Importance of Data Annotation
Annotated data is the heart of supervised learning algorithms because the quality and quantity of annotated data impact the performance and accuracy of such models.
It is significant because machine learning models have a wide range of critical applications and finding high-quality annotated data is one of the key challenges in creating such models.
Conclusion
Whatever your data annotation requirements are, our tools and features are ready to help you in the most efficient and user-friendly way possible. Don’t hesitate to try out the UBIAI Starter Pack here for free.
Start training machine learning models and accelerate your annotations, save time and reduce costs.
