Extracting useful information from documents with automation is an evolving field. Companies have been automating data extraction for years, but unstructured document processing has proved a challenge. Enabling automation of unstructured documents through data extraction is possible but involves multiple technologies. Without a doubt, automating unstructured documents is becoming increasingly non-negotiable for organizations, especially enterprises. 

Structured Vs. Unstructured Documents 

Before implementing process automation for unstructured documents, understanding the differences between structured and unstructured documents is crucial. 

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. 

Can All Data Be Made Structured? 

It is important to bring the convenience of automation to the source of any business transaction.  

This means making use of digitized invoices rather than paper ones and ensuring all documents and records are maintained in digital form on the cloud, rather than on paper. 

It is helpful to use smart mobile applications for registering information in an organized, structured way right at the start. 

However, this is not yet the norm. Most information is still received in an unstructured way and using paper. In fact, paper usage within companies is only growing, particularly in emerging markets. 

So, all data cannot be structured, at least in the foreseeable future. Intelligent unstructured document processing will need to fill in the gap. 

How Traditional Document Data Extraction Works 

Traditional document data extraction for structured documents has been used for years, and is an established field. 

Extracting structured data is a relatively straightforward affair that involves using OCR and RPA with predefined rules to gather the required information and organize it. There is simple automation involved, but nothing more. Technologies like Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP) are not used in traditional document extraction for structured documents. 

Why Extract Unstructured Data? 

Unstructured document processing is essential because there is a host of useful information that can be gleaned from unstructured documents. More useful data means more information with which to make informed business decisions. Also, organizations must implement automation for unstructured documents to save costs on manual data entry that can be expensive, slow, and error prone. 

Here are some unstructured documents from which useful information can be gathered. 

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 

Email correspondence: Sender, recipient, subject, message content, attachments 

Shipping and receiving documents: Shipment number, shipping carrier, weight, consignment destination 

Contracts: Contract number, date, parties involved, terms, expiration date 

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 

Inventory data: Product name, SKU, current inventory level, reorder point, lead time 

Supply chain data: Supplier name, delivery schedules, purchase order history, inventory levels. 

Logistics data: Carrier, shipment tracking, delivery times, freight costs 

As is clear, automation of unstructured documents brings many benefits to businesses. 

Benefits of Automating Unstructured Document Extraction 

Let’s take a closer look at what benefits can be gained from process automation for unstructured documents. 

These are the benefits that are typically experienced when businesses implement automation for unstructured documents.  

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

Technologies and Platforms for Automating Unstructured Document Extraction  

Robotic Process Automation (RPA) 

Traditionally, document extraction has made use of RPA to simulate 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 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 preferred software. 

Artificial Intelligence (AI) 

Artificial Intelligence technologies allow 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 that are fed to them. In the realm of document 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. 

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. 

How to extract unstructured data 

Gathering useful information from unstructured documents is a somewhat complex process that makes use of OCR and RPA along with machine learning, pattern recognition, and natural language processing in tandem. 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. 

First, the documents to be extracted are gathered and prepared. This step may involve tasks like enhancing images and reducing noise to improve accuracy when the extraction takes place.  

Secondly, if there are scanned images or PDFs, these are converted to text with OCR technology.  

Next, relevant data points and information from within each document are identified for extraction.  

The fourth step is where the actual extraction takes place. Multiple techniques including pattern matching, parsing, and extracting based on rules are used to accurately extract the required data. Accuracy at this stage is improved if machine learning algorithms have been used to train the extraction engine.  

The fifth and final step in the process involves verifying and validating the extracted data to ensure accuracy and consistency. The extracted data is compared against pre-defined rules for validation and data quality checks are performed. 

Final Word 

Not all data is structured, at least not yet. The sources from which information must be gleaned are varied, and the quantity of paper being handled within organizations large and small is only increasing. Automating data extraction and unstructured document processing are therefore imperative.