Data Capture:
Definition, Types, Automation in Data Capture

This article provides a comprehensive understanding of data capture, highlighting the technologies involved, their roles in automating processes, and the importance of automation.

Business intelligence team discusses methods of data capture

Every day, businesses are dealing with vast amounts of data that are critical to their operations. Whether it’s customer information, invoices, or supply chain documents, efficient data capture is essential for transforming this raw data into actionable insights.

Data capture technologies—such as OCR (Optical Character Recognition), AI-driven extraction tools, and smart forms—are revolutionizing the way businesses collect, process, and store data.

According to GrandView, the global market for data capture technologies is expected to reach $15.1 billion by 2026, driven by growing demand for automation and data accuracy. Let’s examine data capture and learn:

With real-time data capture, businesses can make faster, more informed decisions, leading to increased efficiency, reduced errors, and enhanced productivity. Let’s get started!

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What is Data Capture?

Data capture refers to the process of collecting and converting information into a digital format that can be easily accessed, processed, and analyzed by computer systems. It is a critical component of modern business operations, enabling organizations to gather data from various sources and integrate it into their workflows for improved decision-making, automation, and efficiency.

Data capture can be manual or automated and is used across industries like healthcare, finance, retail, and logistics to streamline processes and reduce errors. Let’s examine its most common types.

Types of Data Capture

Data capture is fundamental to modern business operations, offering methods ranging from manual entry to advanced technologies like OCR, smart forms, and RFID.

Manual data capture involves the manual entry of data into a system by humans. This could include typing information from paper forms, receipts, or documents into a computer system. Manual data capture is time-consuming and prone to human error but can be necessary in environments where automation isn’t feasible.

Optical Character Recognition (OCR)

OCR is a technology used to automatically capture text from scanned documents, images, or PDFs. It converts printed or handwritten text into digital formats. OCR is widely used for processing forms, invoices, and contracts, significantly speeding up data entry and reducing errors. According to IDC, businesses that use OCR see a 30% increase in processing efficiency.

What Is Data Capture via Smart Forms

Smart forms use interactive, digital templates to capture data directly from users, such as customer details or feedback. These forms are typically found online and are automatically linked to a database, enabling seamless collection and integration of data. They reduce manual input and enhance accuracy by validating the information entered.

How Does Data Capture with Barcode and QR Code Scanning Work?

This method uses scanners to capture information encoded in barcodes or QR codes. It’s common in industries like retail, logistics, and healthcare for tracking inventory, shipments, or patient records. Barcode and QR code scanning offers fast and accurate data capture, minimizing errors and speeding up processes.

READ MORE: Extracting Cost and Inserting Value into Your AP Process

How to Use Radio Frequency Identification (RFID) for Data Capture?

RFID technology captures data through radio waves, often used in inventory tracking or asset management. RFID tags are attached to products or equipment, and readers automatically capture the data when the items pass through designated checkpoints. This method allows for real-time data capture without manual intervention.

With growing reliance on data, selecting the right data capture technology is essential for staying competitive in today’s fast-paced digital landscape. Automating data capture can significantly enhance efficiency, reduce errors, and provide real-time insights that help businesses make informed decisions.

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Using OCR for Data Capture

Optical Character Recognition (OCR) is a technology that transforms images of text into a format your computer can understand.  It starts by taking a picture of the text, either with a scanner, a camera or by using an existing digital image.

This image then goes through some preparation. Think of it like tidying up before starting a big project. The OCR software removes any smudges or stray marks, makes sure the image is straight, and converts it to black and white for clarity. It also figures out the structure of the text, identifying paragraphs, columns, and tables.

Next, the software needs to separate the image into individual characters. It does this by looking for patterns, grouping pixels that seem to form letters, and using its knowledge of different fonts and writing styles.  The software then examines each character, noting its shape, size, and the lines and curves that make it unique.

This information is compared to a vast library of known characters.  The software uses sophisticated techniques like pattern matching, statistical analysis, and even artificial intelligence to find the best match for each character.

Once the text is recognized, the software does some final polishing. It might correct spelling mistakes, use the surrounding words to clear up any confusion, and even try to keep the original formatting of the document.

The end result? You get an editable, searchable text that can be used in all sorts of ways. You can edit it in a word processor, search for keywords, store it in different formats, and even use it in other computer programs. OCR is a powerful tool that makes it much easier to work with information that was once trapped in images.

Bringing Automation to Data Capture

Automation is revolutionizing data capture, transforming manual, time-consuming processes into streamlined, efficient workflows. Traditional data capture involves manually collecting and entering data from various sources, such as paper forms, invoices, and reports, which can be error-prone and labor-intensive.

By integrating automation, businesses can simplify data capture, reduce manual errors, and significantly improve productivity.

Automated data capture technologies like Optical Character Recognition (OCR), Intelligent Character Recognition (ICR), and machine learning can extract data directly from documents—both physical and digital—and convert it into structured formats that are easier to process. This automation allows businesses to quickly process large volumes of data from invoices, purchase orders, delivery slips, and more.

LEARN MORE: Handwriting Recognition: Definition, Uses, Technology

Solutions like docAlpha, InvoiceAction, and OrderAction are examples of automated systems that use AI-driven technologies to capture data from complex documents and categorize, verify, and store it efficiently. We will discuss these in a second.

Benefits of Automating Data Capture

By automating data extraction, companies can increase accuracy, cut down on processing times, and free employees to focus on more value-added tasks, such as strategic planning and analysis:

  • Automation drastically reduces the time it takes to process documents, making workflows faster and more efficient.
  • Automated systems are far less error-prone compared to manual data entry, leading to greater accuracy in document processing.
  • Automated data capture can handle increasing volumes of documents effortlessly, making it scalable for growing businesses.
  • Automated data capture enables companies to access real-time insights from the data, which helps in better decision-making.

By bringing automation to data capture, businesses can streamline operations, reduce costs, and increase productivity—ultimately driving efficiency and competitiveness in a data-driven marketplace.

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Intelligent Data Capture: How to Put It in Action

Data capture technology is a core component of docAlpha, InvoiceAction, and OrderAction, offering automated solutions for transforming document-based information into valuable, actionable data. Here’s how each solution leverages data capture:

docAlpha: Intelligent Data Capture

docAlpha uses AI-powered Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) technologies to extract information from various types of documents, including invoices, purchase orders, and receipts. The data capture capabilities in docAlpha automatically convert scanned images or digital documents into structured data, enabling companies to eliminate manual data entry.

With advanced machine learning and rule-based classification, docAlpha can understand document types and extract data fields, such as customer details, product descriptions, and financial figures, even from unstructured documents. This automation leads to increased accuracy, reduced processing times, and enhanced productivity.

InvoiceAction: Automating Accounts Payable

InvoiceAction builds on docAlpha’s data capture technology specifically for accounts payable (AP). It automates the extraction of key information from invoices, such as supplier names, invoice numbers, line items, and totals, using OCR and machine learning algorithms. This reduces manual data entry errors and ensures that invoices are processed faster and more accurately.

InvoiceAction also performs three-way matching between invoices, purchase orders, and delivery receipts to verify the accuracy of invoice data before payment approval, helping to avoid overpayments and fraud.

FIND OUT MORE: OCR Capture & AI in Document Processing & Management

OrderAction: Streamlined Order Processing with Sales Order Data Capture

OrderAction utilizes data capture technology to automate the extraction of order-related data from purchase orders and sales forms. It captures data such as order quantities, product SKUs, and delivery dates, and integrates this information into the company’s ERP or CRM system.

This automated data capture ensures that order details are processed accurately and quickly, reducing the chances of errors that could lead to shipment delays or customer dissatisfaction. By capturing order information in real-time, OrderAction allows companies to streamline their order-to-cash cycle, ensuring a more efficient fulfillment process.

Benefits of Data Capture in Artsyl Solutions

Automating data capture minimizes the need for manual input, reducing errors and freeing up employee time for more strategic activities. By automating document processing, docAlpha, InvoiceAction, and OrderAction significantly speed up workflows, ensuring that information is available quickly for decision-making.

AI-powered OCR and machine learning improve data extraction accuracy, ensuring that documents are processed without the typical errors associated with manual data entry. Overall, the data capture technology in these solutions can scale with the organization, handling increasing volumes of documents and transactions as the business grows.

As you can see, the data capture technology in docAlpha, InvoiceAction, and OrderAction transforms document processing through advanced OCR, AI, and machine learning, automating workflows, reducing errors, and improving efficiency across financial and operational functions.

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Understanding Data Capture in Detail: Key Things to Keep in Mind

What Is Intelligent Character Recognition — Is It Just Another OCR?

ICR is an advanced version of OCR that can recognize and digitize handwritten text with higher accuracy. ICR uses machine learning to continuously improve its recognition abilities by learning from new handwriting styles.

Intelligent Character Recognition is particularly useful for processing forms that require handwritten information, such as patient intake forms in healthcare or delivery notes in logistics. By capturing data from handwritten documents accurately, ICR helps automate workflows that were previously dependent on manual processing.

What Is the Role of Machine Learning in Data Capture?

Machine learning enhances data capture by allowing systems to learn from historical data and improve their accuracy over time. When paired with OCR or ICR, machine learning can classify documents automatically, identify key fields, and improve the extraction of data even from unstructured or inconsistent formats. It makes data capture more flexible, as the system can adapt to different types of documents, reduce exceptions, and improve efficiency. This technology is vital for automating complex workflows in sectors like finance and insurance.

How Important Is Data Validation in Data Capture?

Data validation is a crucial part of the data capture process, ensuring that the captured information is accurate, consistent, and usable. Once data is extracted, validation checks are applied to verify its correctness—such as ensuring numerical fields contain numbers or that specific fields are filled out. This is essential to prevent errors from being introduced into databases, which could affect downstream processes like billing or reporting. Automated validation rules help streamline workflows and enhance data reliability, reducing the need for manual checks.

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What Is Structured vs. Unstructured Data in Data Capture?

In data capture, understanding structured and unstructured data is essential. Structured data refers to highly organized information that fits into predefined formats, such as tables, spreadsheets, or databases. Unstructured data, on the other hand, does not follow a specific format and can include documents, images, emails, or multimedia files. Effective data capture systems use different methods (e.g., OCR for documents or natural language processing for text) to convert unstructured data into structured forms, enabling easy analysis and decision-making.

Why Is Metadata Capture Important in Data Capture?

Metadata refers to information about the data itself—such as the date it was captured, the author, or file size—that helps in organizing, managing, and retrieving data effectively. Capturing metadata during the data capture process is critical for indexing documents, ensuring proper classification, and supporting efficient searches later on.

Metadata capture is an important feature for document management systems, as it adds context to the captured information and improves accessibility and usability.

Why Should We Invest in Data Capture Automation?

Data capture automation involves using technology such as OCR, machine learning, and robotic process automation (RPA) to automate the collection of information from documents and images. It minimizes human intervention, speeds up processing, and ensures consistency. By automatically extracting key fields, such as customer information or financial data, and integrating them into existing business systems, automation significantly improves workflow efficiency. It’s a powerful solution for industries that handle large volumes of repetitive data, like finance, healthcare, and logistics.

By leveraging these technologies, businesses can make data capture more efficient, accurate, and adaptable to different types of information, leading to smoother operations and better decision-making.

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Final Thoughts: Embrace Data Capture for Growth

Effective data capture isn’t just about collecting information; it’s about transforming that data into meaningful insights that drive business growth. As organizations continue to adopt AI and automation, implementing advanced data capture solutions will be essential to stay competitive. In fact, studies show that businesses using automated data capture systems see a 30-50% reduction in processing times and a significant improvement in accuracy. Now is the time to embrace data capture technology, streamline operations, and unlock the true value of your data.

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