According to Infosource, the Capture Software market exceeded $5B in 2021. Companies seeking to drive down costs and improve customer experience are increasingly turning to electronic data capture software. This article covers various approaches to data capture as well as what data capture software is, how it works, its benefits, and how recent advances in AI are disrupting this massive market.
Data capture, or electronic data capture, is the process of extracting information in a structured or machine-readable format from any structured, semi-structured, or unstructured data source—including documents (paper or electronic), emails, images, video, audio, and text.
Although most applications of data capture focus on documents, recent advancements in artificial intelligence (AI) and machine learning (ML) have enabled modern systems to recognize and capture data from any unstructured data type.
Some common real-world examples of data capture include:
Data capture software has evolved over the decades and now involves a number of steps:
Manual data capture involves humans typing information from documents, emails, and other sources manually into structured machine-readable formats. This approach is expensive, error-prone, and is increasingly being replaced with automated solutions.
To reduce the cost of manual capture, enterprises have increasingly turned to varied automated approaches for data capture. Below is a list of some common automated data capture techniques.
OCR technology has been widely used for over three decades to turn scanned or photographed text into machine-readable text. The usage started in mailrooms where paper documents were first scanned and then digitized using OCR.
ICR is next-generation OCR technology that is capable of understanding and extracting both typed and handwritten text in scanned or photographed documents.
OMR technology determines the presence or absence of marks at a specific location in a document. This technology is widely used to process hand-filled forms.
Barcodes were designed to automate information capture using scanners. They are widely used in retail and supply chains to streamline the movement and sale of goods.
Digital signature adoption has accelerated in recent years, especially during the pandemic. Digital signatures streamline processes such as on- and off-boarding, purchasing, and order processing to automate signature collection move documents through chains of custody faster.
Online, mobile, or digital forms capture data at the source and eliminate the need for manual data entry.
With the rise of robotic process automation (RPA), the decision-making for data capture solutions started shifting from mailroom to line of business and shared services or global business services (GBS) organizations. Business users found OCR and ICR technology hard to set up and use. This led to the rise of intelligent document processing (IDP) solutions that greatly simplified the user interface and allowed users to pick between a number of OCR and document AI solutions to deliver better results, faster.
Bots or web crawlers find and capture information from one or more online sources.
The information encoded in magnetic strips of magnetic swipe cards is captured using readers.
Increasingly the information that used to be encoded in magnetic swipe cards is encoded in microchips on smart cards for greater convenience as well as a higher level of security and privacy. The readers typically use near-field communication (NFC) technology to capture information securely from smart cards.
MICR readers recognize data encoded in magnetic ink-printed machine characters using. This technology is widely used by banks for check processing.
Text capture is an AI solution that classifies and/or extracts the intent or sentiment from text such as instant messages, chatbots, and unstructured documents.
AI solutions are increasingly used to classify and extract information from business emails such to facilitate customer support, help desk cases, and inter-bank settlements.
AI-powered image capture solutions are increasingly being used to validate identity documents (e.g, employee onboarding and Know Your Customer compliance), redact or anonymize personally identifiable information (PII) information, extract nameplate data, and detect damage in images.
Businesses are increasingly relying on CCTV and drone footage for security and visual inspection. AI solutions can automatically extract license plate information, detect crop damage and estimate yield, assess property values and damage, and more.
Businesses are increasingly auditing their customer-facing resources, including sales, customer success, and customer support. Emerging AI solutions analyze the captured recordings to understand customer sentiments and coach employees to improve customer interactions.
Until recently customers have had to rely on multiple point solutions for data capture. This could be IDP or OCR for documents, and separate tools for email, images, video, audio, and text. An emerging category of [unstructured data processing (UDP)](https://super.ai/unstructured-data-processing) platforms allows users to classify and extract information for any unstructured data type.
Emerging UDP platforms have several advantages over OCR, ICR, IDP, and other points electronic data capture software including:
Electronic data capture software has been around for decades. There are numerous ways of capturing data. Recently, data digitization has become one of the top enterprise initiatives. Emerging unstructured data processing (UDP) platforms can greatly simplify data capture by allowing you to extract information from any unstructured data type, quickly and with guaranteed quality. For more information about UDP, check out the following resources:
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