Companies handle increasing amounts of data year-on-year. Much of this data is stored in paper documents, and despite digitization, the paper trail is only growing. A growing number of enterprises have turned to automated data extraction using data extraction services powered by Robotic Process Automation (RPA). For example, many organizations use invoice extraction automation to digitize PDF invoices received from vendors. 

And yet this automation alone isn’t enough. More is needed. AI data extraction is required. 

While extracting data from structured sources like database tables or spreadsheets is possible with pure RPA combined with Optical Character Recognition (OCR), intelligent data extraction that combines multiple cutting-edge technologies is required to handle handwritten or image content. 

Before we move on, it is important to study the difference between structured and unstructured data. 

Structured Vs. Unstructured Data 

Data found within spreadsheets and online questionnaires and surveys can be said to be structured as they adhere to a certain template. On the other hand, data found within emails, images, videos, and scanned copies of handwritten invoices are unstructured as they do not follow a set template and exhibit variations in style and patterns. 

Extracting structured data is a relatively straightforward affair that involves using document scraping tools that offer OCR and RPA with predefined rules to gather the required information and organize it. 

Gathering useful information from unstructured documents is a more complex task that makes use of an intelligent document processing solution that offers OCR and RPA along with machine learning, pattern recognition, and natural language processing. The machine learning component makes a major difference in identifying useful information in an unorganized structure while natural language processing helps convert extracted text to a machine-readable format. 

What is Intelligent Capture (IC)? 

Intelligent Capture (IC) becomes possible when automated data extraction using RPA is combined with AI, Optical Character Recognition (OCR), Machine Learning (ML), and Computer Vision (CV). 

This document capture approach is more powerful than using only RPA because it helps process unstructured data in addition to structured data found in tables within spreadsheets and databases. Unstructured data includes data found in emails, handwritten invoices, images, videos, etc. Any data that does not fit into a template is unstructured data. 

How is Intelligent Capture Different from Traditional Document Extraction? 

Traditional document extraction uses OCR and RPA powered by predetermined rules to extract required data from structured or semi-structured documents. Data extraction services that use this method essentially perform document scraping controlled by pre-determined rules. 

Intelligent Capture does not use data scraping tools powered by OCR alone but organizes the extracted data into actionable information so that it can serve as a trigger for further business processes or automations. In this way, it serves as an intelligent document processing solution. IC does not work solely based on templates or pre-determined rules but does intelligent data extraction using AI, Optical Character Recognition (OCR), Machine Learning (ML), and Computer Vision (CV), so it can handle content variations and unstructured documents in addition to structured, predictable data. 

What are IC’s Benefits over Traditional Document Extraction? 

Intelligent Capture offers multiple benefits over traditional document extraction done by data scraping solutions. 

  • Amplified efficiency: RPA bots infused with AI can process documents faster than humans and ensure faster turnaround times and better operational efficiency. 
  • Increased accuracy: Intelligent capture reduces errors associated with manual/purely RPA document scraping and entry as it has a high degree of accuracy. 
  • Cost savings: Intelligent data extraction helps organizations achieve significant cost savings through improved resource use and reduced labor costs. 
  • Scalability: AI data extraction solutions are virtually infinitely scalable and can handle massive document volumes without necessitating additional manpower. This allows organizations to experience seamless business growth. 
  • Better compliance: Intelligent capture maintains consistent and accurate data extraction procedures, thus ensuring regulatory compliance. 
  • Increased productivity: Intelligent capture enables your team to focus on higher-value tasks, thus increasing productivity over time. 

What Kind of Documents Can Be Extracted With IC? 

Intelligent Capture goes beyond just invoice extraction and makes it possible to gather useful data from a wide array of documents. 

  • Invoices: Invoice date, invoice number, vendor name, amount due, payment terms 
  • Financial statements: Revenue, expenses, assets, liabilities, net income 
  • Purchase orders: Order number, supplier name, order date, items requested, delivery date 
  • Shipping and receiving documents: Shipment number, shipping carrier, weight, consignment destination 
  • Employee records: Employee name, job title, salary, benefits, performance evaluations 
  • Customer information: Customer name, contact information, purchase history, demographics 
  • Contracts: Contract number, date, parties involved, terms, expiration date 
  • Insurance claims: Claimant information, diagnosis, treatment, billed amount, payment status 
  • Medical records: Patient information, diagnosis, treatment, medications, lab results 
  • Marketing materials: Campaign name, target audience, budget, performance metrics 
  • Product catalogs: Product name, SKU, price, inventory level, product descriptions 
  • Sales data: Sales figures, revenue, customer demographics, marketing ROI 
  • Inventory data: Product name, SKU, current inventory level, reorder point, lead time 
  • Email correspondence: Sender, recipient, subject, message content, attachments 
  • Supply chain data: Supplier name, delivery schedules, purchase order history, inventory levels. 
  • Logistics data: Carrier, shipment tracking, delivery times, freight costs 

What Technologies are Involved in IC? 

Robotic Process Automation (RPA) 

Traditional data scraping solutions have made use of RPA to simulate human-performed manual extraction using a step-based approach. The limitation of document extraction only using RPA is that it is pure automation without intelligence to discern between document formats and layouts. The infusion of other technologies in IC overcomes this limitation. 

Optical Character Recognition (OCR) 

Optical Character Recognition technology makes text on images machine readable. It allows one to copy and manipulate text that is in image or PDF format. For example, OCR allows to convert text from screenshots or handwritten documents into a format that can be used online or on a preferred intelligent document processing solution. 

Artificial Intelligence (AI) 

Artificial Intelligence technology allows computers to understand a variety of human inputs and intelligently respond to queries based on them. In the context of document processing, AI allows automated systems that process documents to be queried and provide responses. 

Machine Learning (ML) 

Machine Learning is a subset of AI. It involves repeatedly training algorithms to learn from data fed to them. In the realm of AI data extraction, ML helps in recognizing patterns and templates, extracting only relevant information, and in improving extraction accuracy over time. 

Natural Language Processing (NLP) 

Natural Language Processing focusses on the interaction between machines and human language. It involves programming computers to process and comprehend natural human language. NLP makes use of AI techniques like sentiment analysis and text classification to analyze blocks of text and extract useful insights from unstructured documents like emails and handwritten documents as a response to queries. 

Computer Vision (CV) 

Computer Vision is a subset of artificial intelligence that mimics human vision to process digital images and other types of visual inputs to comprehend patterns, make decisions, and act based on what is seen. It makes use of machine learning models to interpret what is seen. 

Intelligent Document Processing (IDP) Platforms 

Dedicated Intelligent Document Processing (IDP) platforms integrate RPA and other technologies like OCR, AI, ML, NLP, and computer vision to make Intelligent Capture (IC) possible. They improve in accuracy over time and can handle unstructured images, emails, and PDFs in addition to structured data. 

Intelligent Document Extraction

Best Practices to Optimize AI Data Extraction 

Intelligent Capture is a capable approach to document extraction, but it still needs to be set up right. 

  • Design your process in a way that can handle large document volumes without breaking down. Use mobile phone capture if required. 
  • Use the best quality scans possible to boost OCR results. 
  • Have thorough data verification and validation mechanisms in place to ensure data accuracy. 
  • Use a hybrid approach that uses RPA and ML to ensure accuracy with both structured and unstructured data. 
  • Periodically update and train your ML models with relevant data sets to habituate them to various document formats and layouts. This will ensure improved performance over time. 

Final Word 

The days of unintelligent data scraping tools are ending. The Intelligent Document Processing (IDP) category is expected to grow at a compound annual growth rate (CAGR) of 30.1% at least till 2030 according to data from Grand View Research. The market size for this category is expected to touch USD 11.29 billion by that year. 

Intelligent Capture will be responsible for most of this growth. With the paper trail not showing any signs of slowing down, IC will have a huge role to play in both digitization and in making information actionable. 

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