Data labeling in AI-driven document processing: a complete guide for business leaders

Introduction

With studies observing about 70% of AI development time spent on data preparation and data labeling, business leaders and technical teams can see both the urge and the opportunity to adopt AI-powered, document processing software that follows proper data training. 

As organizations rely on intelligent document processing (IDP) to accelerate operational workflows, extract insights, and drive decision-making, the quality of underlying data labeling processes has emerged as the decisive factor separating successful implementations from costly failures.

Table of contents

• What is data labeling?
• Types of data labeling
• Data labeling techniques
• Benefits of data labeling
• Applications - Industry-based examples
• Conclusion

What is data labeling?

Data labeling is the pillar of machine learning (ML) and artificial intelligence (AI) functions: it’s the process of identifying raw data formats (text, images, videos, etc.) and assigning them labels that specify their categories and contextual elements.

When choosing an AI-based automation software, the quality of the system depends on how well it has been trained.

Yet, feeding raw data to a ML model is not going to work per se. First, ML engineers need to “describe” that data for the AI, for it to distinguish its properties.

In this sense, labeling is a primary step to cluster different types of data.

How data labeling differs from data annotation and categorization

Data labeling is a specific branch of a bigger process: when working on the quality and structure of the data, it differs from data categorization and data annotation because:

1. It doesn’t just aim to assign data to certain clusters, but to create an intelligible structure for Machine Learning models.
2. Just like data annotation, labeling describes information so that algorithms can decipher it and use it. Yet, data labeling involves the type of data (for instance, text or image) so that a more profound structuring can happen via the annotation 

Without these tags, not only the ML models would have a hard time recognizing which data they’re dealing with, but they’d diminish their accuracy with paramount functions such as pattern recognition, prediction making, and creation of automations. 

As we’ll see later here, data labeling interconnects with the supervised, or semi-supervised learning of AIs and ML models (whilst unlabeled data is best for unsupervised learning).

Types of data labeling

Different data labeling techniques depend on the types of data we want to identify and cluster.

Natural language processing (NLP) and data labeling

NLP is a branch of AI that blends semantic language generation and recognition with statistical computing. 

This helps ML and deep learning models to identify and label text for qualifying data as suitable for training purposes. 

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This is especially useful for businesses where accuracy is fundamental, like in financial digitized operations, document processing for healthcare, tax-related and administrative tasks, as well as in small companies looking to speed up dramatically their day-to-day document processing and data extraction. 

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A core NLP-based data labeling type is text labeling.

Text labeling

Text labeling involves annotating textual data, including human written communication and text in images.

This includes:

• Named entity recognition (NER), to identify elements like names, dates, and locations.
• Sentiment analysis and intent recognition, to categorize text as positive, negative, or neutral, and label customer inquiries in chatbots and virtual assistants.
• Document classification, to categorize emails, invoices, and contracts based on content with the highest possible level of accuracy.

Computer vision

Computer vision labeling refers to the identification of objects in pictures via algorithms capable of recognizing data labels. These algorithms distinguish both the type of picture and the objects in it. 

The same applied for images in motion: without computer vision, analyzing the frames of a video would be hard and inaccurate. 

Image labeling and applications

Image labeling teaches AI applications to see in computer vision. Common use cases include:

• Object detection, for instance, to identify objects like pedestrians, vehicles, or products in images.
• Facial recognition, which enables the recognition of faces for security or personalization.
• Medical imaging, to label X-rays and MRI scans and detect anomalies.
• Retail analytics, to recognize shelf inventory and consumer behavior.

Video labeling

Video labeling involves annotating moving images in relation to time frames to train models for use cases like:

• Autonomous driving, by identifying traffic signals, road signs, and pedestrians.
• Security applications, like detecting suspicious activity in surveillance footage.
• Content editing, which recognizes specific frames and apply AI functionalities to them

Audio labeling

Audio labeling teaches computers to hear and activate speech recognition. Examples include:

• Speech-to-text transcription, or converting spoken words into text.
• Speaker identification, recognizing individual voices.
• Emotion detection, extracting sentiment in voice recordings.
• Voice assistants, which leverage audio labeling as a source of input to then operate

3D point cloud labeling

3D point cloud labeling is used in applications that require spatial awareness, such as:

• LiDAR-based navigation, used to create 3D maps for autonomous vehicles and robotics.
• Augmented reality (AR) and virtual reality (VR), which map real-world objects for digital applications.
• Urban planning, to model city infrastructures for simulations.

Data labeling techniques

Implementing data labeling requires a strategy per se, as different techniques can impact time, resources and quality of work in engineering teams.

Here following, some of the main techniques.

Manual labeling

This is the "old school" approach where humans carefully label each piece of data by reviewing and tagging datasets manually. This method offers high accuracy but is time-consuming and expensive.

Pros: very accurate, especially for complex or nuanced data.

Cons: expensive and slow – doesn't scale well.

Automated and hybrid labeling

Automated labeling makes AI-driven tools label data using pre-trained models, which speeds up the process but may require human verification for accuracy.

If there’s margin for error tolerance and an extremely urgent need of closing an ML training project, this is a suitable method.

Pros: fast, relatively cheap, and scalable.

Cons: can be less accurate than manual labeling, especially with unfamiliar data.

Yet, advanced and effective data labeling requires HITL (Humans In The Loop), which means involving people to guide the training, fine-tuning and testing of ML models.

This hybrid technique is the foundation of semi-supervised training, which is widely adopted for its flexible set up. 

In fact, second-generation labeling systems combine human expertise with machine intelligence through techniques like:

• Active learning: workflows that prioritize ambiguous documents for human review
• Multimodal validation: cross-checking text, tables, and embedded images
• Semantic clustering: auto-grouping similar documents to accelerate batch labeling

Pros: balances speed, cost, and accuracy, making it a popular choice for many businesses. Cons: requires careful management to ensure the AI and human labelers work well together.

Programmatic labeling

Instead of humans or AI, this uses code and rules to generate labels. Rules-based algorithms and scripts generate labels based on predefined logic.

This method is efficient for large datasets but requires careful calibration.

Pros: highly repeatable and can be very accurate if the rules are well-defined.

Cons: requires a thorough technical expertise to set up and maintain, and doesn't work well for complex or unstructured data.

Crowdsourcing

Crowdsourcing platforms distribute labeling tasks among multiple workers (usually outsourced), reducing time and cost. However, inconsistent labeling quality can be a challenge.

Pros: can be relatively cheap and scalable.

Cons: quality control can be a challenge, as you're relying on the expertise and diligence of many different people.

Benefits of data labeling

Other than being a fundamental aspect of the ML training process, data labeling entails several collateral benefits.

Model accuracy

Raw benefit: improved AI model accuracy and more precise predictions of the outputs.

Think of data labeling as giving your AI a high-quality private education. The better the "textbooks" (labeled data), the smarter and more reliable your AI becomes. 

Automation improvement

Raw benefit: enhanced performance for automated document processing and data extraction.

When correctly targeted, data labeling drastically improves the performance of AI in its process to understand and process complex documents (like contracts, invoices, etc.) and extract key information. 

The more accurate this operational aspect is, the more safely businesses can leverage it to earn time and resources, and focus on more valuable tasks.

For instance, Procys platform leverages hybrid techniques to train its ML models and provide maximum accuracy with its intelligent document processing.

Insightful, informed decision-making

Data labeling doesn’t just prepare the AI for training, but enables leaders to keep a detailed track of how data are used.

In fact, the categorization of labeled information, allows us to identify trends, patterns, and anomalies that would otherwise be hidden. 

Data usability just gets better

The data variables within a model might need to change or be reclassified (for instance, when an AI is not picking up some data as expected).

This all rounds up to create a more qualitative ratio for the data we use, other than improving the level of granularity when controlling the variables we introduce, the labels and categories we set-up and the data transitions we activate.

Applications - Industry-based examples

Modern Intelligent Document Processing (IDP) platforms that manage documents like invoices, contracts, and regulatory filings require labeling precision at over 99% to maintain operational reliability.

For instance, a single mislabeled date field in a supply chain contract or improperly categorized line item in an invoice can cascade into compliance violations, payment errors, and broken automation workflows.

For business leaders looking for hand-on applications of software trained with data labeling techniques here is a list of examples.

AI-powered document processing

AI-driven OCR and data extraction tools use labeled data to categorize invoices, contracts, financial statements and other documents efficiently.

In fact, the proper software can accelerate up to six times the work of operational teams looking to stay ahead of the curve (and of the competition).

Fraud detection in finance and banking

Labeled datasets help detect anomalies in transaction data, preventing fraud and ensuring compliance with financial regulations.

Moreover, studies show that financial institutions using high-quality labeled training data execute  loan processing 70%+ faster and reduce by 60% the costs of document review.

Healthcare diagnostics with AI-based imaging

The healthcare sector provides a compelling case study, where medical billing teams using AI-assisted labeling reduced claim denial rates by 41% through improved ICD code matching in patient records

Furthermore, AI models trained on labeled medical images assist doctors in diagnosing diseases, reducing human error.

Retail and e-commerce recommendations

Data labeling is the secret ingredient powering personalized shopping experiences.

We can picture an online store that knows its customer desires better than they do: that's the result of meticulously labeled product images, descriptions, and customer interactions.

In fact, labeling product attributes (like "color," "material," or "style") allows AI to generate tailored recommendations, and fuel visual search, letting customers find items simply by uploading a picture.

Finally, back-end wise, labeled transaction data enables fraud detection systems that protect both businesses and consumers from fraudulent activities, creating a backbone of secure, personalized, and efficient shopping.

Autonomous vehicles and smart cities

Data labeling acts as the AI tutor that teaches machines to understand our world.

For autonomous vehicles, it's the process where humans tag sensor data – for instance, marking pedestrians as "walkers," brake lights as "stopping signals," and construction zones as "hazard areas."

This labeled data trains AI to make split-second decisions, like a self-driving car distinguishing between a plastic bag blowing across the road (ignore) and a child chasing a ball (emergency stop).

For the planning of smart cities, it transforms raw traffic camera feeds into actionable insights – labeling "rush hour congestion patterns" versus "accident-related gridlock," enabling dynamic traffic light adjustments that reduce commute times by up to 30% in cities like Barcelona.

For smart cities, some studies showed that labeled sensor networks can help predict infrastructure failures 72 hours in advance, cutting maintenance budgets by 19% annually.

Conclusion

Data labeling enables accurate model training, enhances automation, and improves decision-making across industries. 

Procys can classify, extract, and validate data from various document formats, involving ML engineers to validate and pre-label data, so that users can automate document processing and focus on what matters: their business.

Try Procys for free here.