Artificial Intelligence (AI) is a promising market worth around 327 billion US dollars. Its influence extends far beyond economic sectors, affecting various aspects of our lives: healthcare, automotive, insurance, banking, tourism, among others.

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However, behind this technological advance lie essential techniques for teaching computers (Machine Learning). This uses powerful, data-intensive learning algorithms to provide answers that are as accurate and complete as possible.

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To fully exploit AI, a company must use high-quality annotated data to improve the training of its Machine Learning (ML) model. Data annotation is one of the essential methods for feeding data into an AI or ML model.

What is Data Labeling? Why is this method necessary? What techniques are used to annotate data? Docloop gives you a comprehensive overview of data labeling and its importance in the development of artificial intelligence.

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Data annotation is the process of labeling raw data to improve the learning of AI and ML models. Images, videos, text and similar formats need to be annotated to enable models to recognize and interpret them effectively.

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Data annotation relies on appropriate methods and tools to mark, transcribe or process objects in various types of information or content. In concrete terms, the quality of the annotated data determines the performance of the machine learning model.

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Have you ever thought about how a driverless car works? Without the use of annotated data, these autonomous vehicles would be unable to distinguish their environment and would drive straight into a wall. Since machines have no innate knowledge of the physical world, they have to learn to distinguish between the objects and images they capture.

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Similarly, many companies are investing in learning AI/ML models to automate the classification of their business documents. This is where Data Labeling comes in, enabling algorithms to distinguish between different types of document, such as supplier or customer invoices.

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The main advantage of Data Labeling is that it saves time and money. By improving the accuracy of machine learning models, it speeds up data processing.

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Data Labeling also offers other advantages for companies:

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• Greater precision: correct data annotation guarantees accurate results, thus improving algorithm learning.

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• Greater efficiency: Data Labeling facilitates the training of AI/ML models, enabling them to better recognize texts, objects and intentions.

‍Reduced human intervention: accurate data labeling improves the quality of results from machine learning models, significantly reducing human intervention.

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Data annotation can be carried out manually by Data Labeling experts , or automatically by artificial intelligence systems, or both (semi-automated).

Manual data annotation

Data Labeling experts manually label raw data according to specifications and expected results. They use various methods to annotate relevant elements, such as the key point tool or the bounding box (Bbox).

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However, although manual labeling is more accurate, it is time-consuming and difficult to implement. For example, annotating a single image takes an average of fifteen minutes, depending on format quality, requirements and the annotation tool used. 

Suppose you have a project to annotate 10,000 images. For an expert annotator, this represents 2,500 hours of work, or 15 months at a rate of 8 hours per day, not including weekends. Imagine the time it can take for a project of 30,000 or 50,000 images.

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Automated data annotation

Automated annotation, on the other hand, uses artificial intelligence systems to label data. This methodspeeds up the process, based on conditions and rules previously established by humans. 

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However effective, automatic data annotation can be limited by frequent changes to data structures. As a result, it becomes difficult to establish precise rules to guide systems in labeling.

While humans can easily distinguish between Coke and custard, machines are unable to accurately identify subtle visual elements. Consequently, the intervention of an expert annotator is essential to ensure the quality of data labeling.

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Several sectors use data labeling to develop machine learning models essential to their growth, such as :

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• Healthcare: labeling medical images, clinical notes and electronic medical records(EMRs) is an excellent way to design computer vision devices.

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• Retail: annotating product images and customer data enables AI/ML systems to be trained to improve the customer experience or recommend products.

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• Finance: corporate financial data can be annotated to develop AI/ML models capable of detecting fraud or standardizing other financial processes.

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If you're developing an AI/ML project, here are three main pieces of software to try out for annotating your raw data:

Docloop

Does your company specialize in transport and logistics? Docloop offers an efficient tool for extracting, classifying and transferring your data in total security. Our technology automatically processes all your business documents without any intervention on your part.

Labelbox

This renowned platform makes it easy to create, manage and implement data annotation projects using collaborative online tools. Labelbox supports a variety of formats, including text, images and video.

RectLabel

Designed for Deep Learning projects on the Mac, this data annotation software features semantic segmentation and object detection. RectLabel provides labeling suggestions thanks to its integrated machine learning model.

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We live in a data-driven world, where AI/ML models need to absorb large amounts of data to continuously improve. This is why data annotation is essential in the development of an artificial intelligence or machine learning project.

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Data Labeling is a long and tedious process, requiring specific skills and the use of suitable tools. Labeling can be manual or automated, depending on the volume of data to be annotated and your company's needs and budget. In any case, the effectiveness of the AI/ML model depends on the quality and quantity of the annotated data.

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Due to the complexity of this work, many organizations turn to external service providers to annotate their data. This saves time, increases efficiency and delivers quality results. At Docloop, we have the tools to help logistics companies automate the processing of their documents.

Stop wasting time manually keying in your customer/supplier invoices and book a demo of Docloop's services today!