What is image segmentation: types, characteristics and applications

Introduction

To understand what image segmentation is, we need to dive into its core functionalities, main applications, and how it differs from other forms of image annotation.

In fact, image segmentation is a critical technique for a cascade of intelligent document processing applications: without it, the “simple” recognition and isolation of objects in an image would be impossible. 

By dividing – or segmenting – the core parts of images, this process enables machines to understand and classify visual information more efficiently. 

With the goal of helping tech professionals understand image segmentation, and to aid businesses make an informed decision over an automation software, here we will explore key types and applications, with realistic scenarios.

Table of content

• What is image segmentation?
• 3 types of image segmentation
• Image segmentation methodologies - Quick view
• 5 subtypes of image segmentation
• Applications of image segmentation in data extraction
• The role of image segmentation in intelligent document processing

What is image segmentation?

Image segmentation is a sophisticated computer vision process that divides a digital image into distinct pixel clusters, known as image segments.

By leveraging this function, AI models can analyze images with higher accuracy, making it a cornerstone of many modern applications.

For this, it plays a vital role in object detection, classification, and recognition by separating objects from the background and other elements: for how simple this may look, it is a core part for an enormous variety of work applications.

In fact, we can recognize it in multiple domains and use cases, including medical imaging, satellite image processing, document digitization, and machine learning.

The first step to understand image segmentation is to explore some of the most used types and techniques. 

3 types of image segmentation

There are several approaches to image segmentation, each with its own advantages and use cases. Following through, we have listed three main types and five more sub-types of image segmentation.

1. Semantic Segmentation

Semantic segmentation assigns a class label to each pixel in an image, meaning that all pixels belonging to the same object category share the same label.

However, it does not differentiate between individual instances of the same class. For example, in an image of a street scene, all cars would be labeled as "car," but they would not be distinguished as separate entities.

How it works

• Uses a pixel-wise classification approach where each pixel is classified into predefined categories.
• Often implemented using fully convolutional networks (FCNs) and deep learning models to generate dense segmentation maps.
• Commonly trained on large labeled datasets such as Pascal VOC, COCO, and Cityscapes to recognize general objects.

Real-world applications

• For autonomous vehicles: identifying roads, pedestrians, vehicles, and obstacles to aid navigation.
• In medical imaging: for instance, segmenting organs, tumors, and tissues in CT or MRI scans.
• For satellite imagery: differentiating land, water, and vegetation in geospatial analysis.

Limitations

• Cannot separate individual objects within the same category.
• Struggles in crowded scenes where multiple objects of the same class overlap.

2. Instance Segmentation

Instance segmentation builds upon semantic segmentation but distinguishes between individual instances of the same class.

Instead of labeling all objects of a category with the same color, it assigns a unique mask to each separate object. For example, in an image with five cars, instance segmentation identifies each car as an independent object.

How it works

• Uses bounding boxes and segmentation masks to separate individual objects.
• Typically implemented using Mask R-CNN, an extension of Faster R-CNN that adds a segmentation branch to generate pixel-wise masks for each detected object.

Real-world applications

• Inventory Management: as it identifies and counts products or items.
• Surveillance and security: with its capability to recognize and track individuals separately in crowded places.
• Accounting: using OCR to precisely extract text and data from scanned financial documents such as invoices, receipts, and contracts

Limitations

• Computationally more expensive than semantic segmentation.
• Can struggle with overlapping or occluded objects in cluttered environments

3. Panoptic Segmentation

Panoptic segmentation is a hybrid approach that merges the best of semantic and instance segmentation.

It provides a comprehensive understanding of a scene by categorizing every pixel while also distinguishing individual objects.

This type of segmentation classifies objects into two broad groups, which we can call:

• "Things" (countable objects like people, cars, trees, animals)
• "Stuff" (amorphous regions like sky, road, water, grass)

How it works

• Uses dual-branch neural networks—one for semantic segmentation and another for instance segmentation.
• Models like Panoptic FPN (Feature Pyramid Network) and Panoptic-DeepLab combine object detection with pixel-level segmentation.
• Provides a holistic scene representation, making it useful for applications requiring both object differentiation and background understanding.

Real-world applications

• Smart cities: performing traffic analysis where vehicles and pedestrians are individually recognized, while roads and sidewalks are segmented as background.
• AR and VR: To enhance immersive experiences by distinguishing between interactive elements and static environments.
• Autonomous navigation: advanced robotic vision where self-driving cars must understand their surroundings in depth.

Limitations

• More computationally demanding than either semantic or instance segmentation alone.
• Requires careful model design to balance object detection and pixel-wise classification.

Image segmentation methodologies - Quick view

‍

5 subtypes of image segmentation

Moreover, we can classify several other methodologies that fall into one of the three clusters mentioned above.

These describe the operational method that a software with image segmentation ingrained can use, yet choosing a software for its capabilities should always be paired with use cases and  business needs.

1. Threshold-Based Segmentation

Category: Semantic Segmentation

This method involves setting a threshold value to classify pixels as either part of an object or the background. It works well in cases where the contrast between the object and background is high.

In fact, thresholding classifies pixels into distinct groups based on intensity or other criteria, making it suitable for identifying broad classes (e.g., foreground vs. background) without distinguishing individual instances.

2. Edge-Based Segmentation

Category: Semantic Segmentation

Edge detection algorithms, such as the Canny and Sobel filters, identify the boundaries of objects based on pixel intensity differences, which helps define classes but does not inherently differentiate instances within the same class.

This method is useful when clear edges exist between different regions in an image.

3. Region-Based Segmentation

Category: Semantic or Instance Segmentation

Region-based methods group pixels into regions based on similarity (e.g., color, texture and other similar properties).

Methods like region growing and watershed segmentation fall under this category, as Depending on the implementation, it can be used for semantic segmentation (grouping similar areas) or instance segmentation (identifying individual objects).

4. Clustering-Based Segmentation

Category: Semantic or Instance Segmentation

Clustering groups similar pixels into segments. Unsupervised machine learning techniques like k-means clustering and mean shift clustering are used to partition an image into different clusters based on pixel characteristics, while more advanced clustering (e.g., hierarchical or fuzzy clustering) can distinguish instances of the same class

5. Deep Learning-Based Segmentation

Category: Semantic, Instance, and Panoptic Segmentation

Deep learning models, such as convolutional neural networks (CNNs), can perform all three types of segmentation depending on the architecture and training data.

These models provide highly accurate and automated segmentation results, for instance:

• Fully Convolutional Networks (FCNs) are used for semantic segmentation.
• Mask R-CNN is designed for instance segmentation.
• Panoptic FPN combines both to achieve panoptic segmentation

Applications of image segmentation in data extraction

By breaking down complex visual information into manageable components, image segmentation has paved the way for groundbreaking applications across diverse sectors.

Let's explore some of the most impactful and innovative applications of image segmentation across various industries.

Medical imaging

Image segmentation is widely used in healthcare to detect tumors, classify tissue types, and assist in medical diagnosis through MRI and CT scans.

Autonomous vehicles

Self-driving cars rely on segmentation to differentiate between pedestrians, vehicles, traffic signals, and road signs.

Document processing and OCR

AI-powered optical character recognition (OCR) tools use segmentation to extract text from scanned documents, invoices, receipts, and contracts with precision. This improves efficiency in finance, legal, and administrative sectors.

Satellite and aerial imagery

In geospatial analysis, segmentation is used for land cover classification, disaster assessment, and urban planning by distinguishing different terrain types.

Retail and e-commerce

Product identification, inventory management, and automated checkout systems leverage segmentation to improve operational efficiency.

The role of image segmentation in intelligent document processing

While image segmentation is commonly associated with computer vision tasks, it also plays a crucial role in document processing.

This process falls under Intelligent Document Processing (IDP), the practice involving  the combination of OCR (Optical Character Recognition), machine learning (ML), and natural language processing (NLP) to automate data extraction and classification from documents.

In fact, IDP can leverage image segmentation to distinguish between different elements within a document, such as text, tables, logos, and handwritten notes, ensuring precise extraction and categorization.

By segmenting documents into meaningful sections, IDP systems can:

• Recognize patterns
• Eliminate manual data entry
• Collaborate with other systems like accounting software, ERP, and CRM platforms using ad-hoc integrations.

Image segmentation as core enabler to automate accounting at scale 

After a study on 570+ C-suite executives, BCG found that business leaders “achieved only an average of 48% of their cost-saving targets in 2024, and most say their companies struggle to maintain cost efficiencies”. ¹

Now, let’s think of an accounting team at a mid-sized company, receiving hundreds of invoices from different vendors every month, often in varying formats (PDFs, scanned images, and email attachments).

Traditionally, employees must manually extract key details such as invoice number, vendor name, due date, line items, and total amount, which is time-consuming and prone to errors.

By implementing an AI-powered document processing system with image segmentation, an AP team can automatically extract and classify key data from invoices in a structured manner:

• Pre-processing: image segmentation detects and separates different invoice components, such as headers, tables, and footers, making it easier for OCR technologies to extract the relevant information.
• Data extraction: the system identifies and extracts data like invoice numbers, dates, tax amounts, and line items, even if they appear in different formats across vendors.
• Validation and matching: AP teams can cross-check the extracted data automatically against purchase orders (POs) and payment records to ensure accuracy before approval.
• Approval workflow: after the invoice scanning and match validation, the accounting team can forward the invoices automatically for approval based on predefined rules, reducing delays.
• ERP & Accounting Integration: finally, the responsible team can enter approved invoices into other accounting software or ERP with a couple of clicks, eliminating manual data entry.

This example shows how image segmentation blends into an IDP procedure, not only speeding up document workflows but also improving compliance, accuracy, and efficiency in operational processes.

AI-powered automation platforms like Procys utilize segmentation to extract key information from invoices, receipts, and other structured documents with high accuracy. Try it for free here.

Finally, some core benefits that business can achieve for their accounting teams are:

• Significant reduction in manual data entry workload
• 6x faster invoice approvals and document processing, preventing late payments and penalties.
• Lower error rates, improving financial accuracy and compliance.
• Seamless ERP integration, enabling cross-collaboration between systems and departments.

Complementary tools and technologies for image segmentation

In the mission of providing software capable of leveraging image segmentation effectively, providers of intelligent solutions can use a variety of complementary tools and frameworks.

Some of them are:

• OpenCV – popular open-source library with various image processing functions.
• MATLAB – which has built-in segmentation functions and deep learning support.
• Google TensorFlow – which includes pre-trained deep learning models for segmentation.
• Labelbox – a data labeling tool for training AI segmentation models.

Challenges in image segmentation

The tenure of creating robust image segmentation features depends on its challenges: software providers like Procys work to tackle critical limitations that can make or break the correct functioning of this technology, with the mission of augmenting the quality of their proprietary solutions.

Without this work, advanced functions like AI-auto split would be impossible to activate.

Some of these challenges are

• Complex backgrounds: when objects have similar color or texture to the background, segmentation becomes more and more difficult.
• Occlusion and noise: poor image quality and overlapping objects are a stick in the mud that can reduce the accuracy of the function.
• Computational demands: deep learning-based segmentation requires significant processing power: for this, software providers that can’t find the balance to operate these systems at scale, will not be able to offer accessible prices to businesses.
• Data scarcity: high-quality and voluminous annotated and labeled datasets are the pillar for training AI segmentation models.

Conclusion

The use of image segmentation in document digitization ensures that critical information is captured and processed seamlessly, improving overall business productivity.

Image segmentation powers various AI-driven applications across industries, helping machines interpret visual data with precision.

Relying on robust and accessible software is the first step to accelerate manual tasks related to several document processing tasks, including accounts payable ones.

Finally, image segmentation is a core component to achieve increased accuracy and transform the way of working for businesses looking to reduce operational costs.

‍

Sources

‍1: One-Third of Corporate Leaders List Cost Management as Their Most Critical Priority for 2025, BCG, 2025