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Data Extraction
Jan 19, 2023
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Document Extraction: Automatically Extracting Data From PDFs, Images, and More

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Chief AI for Everyone Officer

What is Document Data Extraction?

Document data extraction, also known as document extraction, document capture, intelligent capture, and document automation, is a technology that helps organizations quickly and accurately extract information from any document. The process involves using automated methods to scan and extract data from documents such as text, images, and tables. The extracted data can be used for various purposes, including powering business intelligence, automating processes, and providing customer service.

Manually gathering and organizing data from various sources can be a time-consuming and resource-intensive task, taking away valuable time and energy from a company's human resources. Automated document extraction is becoming a popular solution in various industries such as finance, healthcare, and government for the extraction of strategic data from such documents as invoices, contracts, statements, and reports. The data extracted can be combined with or integrated into other automation tools for subsequent processing steps and to gain valuable insights that allows for more strategic decision-making and efficient business operations.

There are a variety of automation tools that can help with extracting data from a range of documents. These tools are based on technologies such as optical character recognition (OCR), computer vision (CV), natural language processing (NLP), machine learning, fuzzy matching, supervised or reinforcement learning, and, more recently, Large Language Models (LLM). OCR is used to recognize the text on documents, CV is used to break a document into sections, while NLP helps the extraction process by understanding natural language and extracting data from it. Machine learning algorithms are then trained on the data to recognize patterns and extract the desired information. Supervised or Reinforcement learning is used to learn from human feedback to improve extraction results. Fuzzy matching and LLMs are used to recognize key/value pairs accurately, even when the wording varies greatly.

The market for data extraction software is slated to see substantial growth in the coming years, thanks to the rising popularity of machine learning and AI technology. The increasing use of cloud-based solutions is also expected to play a significant role in the market's expansion. According to Verified Market Research, the automated data extraction market size was valued at approximately 1.2 billion USD in 2021, and is projected to reach nearly 4 billion USD by 2030.

Business documents and data to extract

Document data extraction is becoming increasingly important in business as orgniazations look for ways to streamline and improve processes. Some document types such as invoices, sales orders, and contracts are used by almost every business. Then there are documents industries/verticals specific - Explanation of Benefits in healthcare, First notice of loss (FNOL) and claim forms in insurance, annual reports and financial statements in financial services, bills of lading in retail, manufacturing, and logistics, etc.

Some common documents that are used in business operations, and the types of data that must be extracted from them for subsequent use include:

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

Document data extraction can be used to reduce costs associated with manual processing, improve vendor experience by automating payments, improve customer experience by speeding up issue resolution, improve decision-making with faster and more accurate data, and finally lower risks by reducing errors and maintaining proper audit trails.

Benefits of automated document data extraction

Document data extraction technology allows organizations to reduce costs, improve customer experience, and reduce risks. Here are some of the critical benefits of document data extraction:

  1. Cost Savings: Document data extraction automates the process of extracting data from documents, saving time and labor. According to Glassdoor, the national average salary for a Data Analyst in the United States is $72,299. If a company has 5 Data Analysts working on manual data extraction from documents, and each analyst works for a full-time role (40 hours per week), the cost of employing these data analysts would be: $361,495 (annual salary cost), not including other perks and benefits that must be offered. But that’s not all. It has been reported that 80% of a data scientist's time and effort is spent on collecting, cleaning, and preparing data for analysis. Therefore, for a company with 5 data analysts, the cost of data preparation alone would be $289,196, which leaves far less for other strategic activities that the data analyst is equipped to handle.
  2. Accuracy: The likelihood of human error when manually inputting data into basic spreadsheets ranges from 18% to 40%. These errors are not always the result of incompetence but rather the inherent fallibility of human beings. Errors are serious issues not only because they affect the business operations, but also because they cost more. According to the 1-10-100 rule, the cost of preventing mistakes and verifying data is $1, while the cost of fixing an error is $10 and the cost of repairing the damage caused by the error is $100.
  3. Scalability: The expansion of a business necessarily accompanies an increase in the volume of documents and data that must be managed. For example, if a company that has scaled up must now process 10000 invoices a day, manual operations using its existing team of 5 data analysts would take 2 weeks. An automation tool can process this in less than a day, freeing the analyst to do actual business analytics rather than data management. Companies can then better leverage the insights from data to inform business decisions, improve operations, and drive growth.
  4. Efficiency: In addition to saving time and money, automation of mundane activities like data extraction can free the skilled human to perform more strategic tasks that benefit the organization. This increases efficiency and helps businesses achieve their goals faster.
  5. Reduce risk: Improved accuracy, automated audit trails, and faster processing help organizations maintain regulatory compliance, lowering the overall risk.

Document data extraction solutions

Optical Character Recognition (OCR)

One of the most popular methods of document data extraction is Optical Character Recognition (OCR). Invented more than 30 years ago, OCR is an automated process that converts scanned or digital documents into machine-readable text. OCR software can recognize text in various languages and formats, making it a powerful tool for document data extraction. OCR is mainly used for extracting text from documents such as PDFs, scanned images, and other printed documents.

When using OCR, the document is first converted into an image, and then the image is analyzed by the OCR software. The software then attempts to identify the characters and patterns in the image, and convert them into text.

In the last ten years, traditional OCR vendors as well as cloud providers such as Google, Microsoft, and Amazon have introduced a more advanced version of OCR that use AI and ML capability within them for improved data extraction.

However, OCR is only the first step in document data extraction. OCRs typically turn the entire page into text and provide position information for each section of text in the document in a JSON format. Most OCRs also provide key/value pair and table information. However, users still need an additional tool to extract the desired information from the OCR output. They are not designed to be used by business users.

Popular OCRs include:

  1. Tesseract (open source)
  2. ABBYY Finereader
  3. Microsoft Azure Form Recognizer
  4. Google Document AI
  5. Amazon Textract

Intelligent Document Processing (IDP)

Intelligent Document Processing (IDP) is a technology that uses Optical Character Recognition (OCR) and Machine Learning (ML) to automatically extract data from unstructured documents such as invoices, receipts, and forms.

The process typically begins with OCR, which uses image-recognition algorithms to convert scanned documents into digital text. This text is then passed through a series of ML-based algorithms, which analyze the structure and content of the document to identify and extract specific pieces of information.

The extracted data is then verified by the system and passed through a series of validation and quality assurance checks before being exported to a target system, such as an ERP or a CRM.

Here are some of the available IDP solutions:

  1. Automation Anywhere IQ Bot
  2. Workfusion
  3. Hyperscience

Unstructured Data Processing (UDP)

Unstructured Data Processing (UDP), also referred to as next-generation IDP or Intelligent Content Processing, solutions are a more advanced version of the IDP solution. UDPs commonly use a combination of natural language processing (NLP) and machine learning (ML) algorithms.

NLP is the field of artificial intelligence that deals with the interaction between computers and human languages. It is used to extract meaning from text and speech data. NLP techniques such as tokenization, stemming, and lemmatization are used to pre-process the text data before it is fed into machine learning models.

Machine learning, on the other hand, is a method of teaching computers to learn from data without being explicitly programmed. The goal of machine learning is to develop models that can automatically extract insights from data. Supervised learning, unsupervised learning and reinforcement learning are the three main categories of machine learning algorithms used in UDP.

  • Supervised learning algorithms are used when labeled data is available. They are trained to predict a target variable based on a set of input features. Common supervised learning algorithms used in UDP include Logistic Regression, Random Forest, and Naive Bayes.
  • Unsupervised learning algorithms are used when data is unlabeled. They are used to find patterns and structure in the data. Common unsupervised learning algorithms used in UDP include Clustering, Principal Component Analysis (PCA), and Singular Value Decomposition (SVD).
  • Reinforcement learning algorithms are used when data is sequential. They learn by interacting with the environment and receiving feedback on the actions they take. Common reinforcement learning algorithms used in UDP include Q-Learning and SARSA.

Once the machine learning models are trained, they are used to extract insights from the data. The results are then visualized using various techniques such as charts, graphs, and tables to make it easier for humans to understand and interpret.

The difference between IDP and UDP solutions is that UDP solutions are able to process any unstructured data - documents, images, videos, audio, and text. In addition to extracting information, UDP solutions can also classify, redact, and answer questions about unstructured data. This makes them ideally suited to become unified AI platforms that are adopted along with RPA for a wide variety of intelligent automation use cases.

Since UDP solutions are more flexible than IDP solutions, they can process more complex documents that include nested tables, stamps/watermarks, signatures, and handwritten notes. Super.AI is one of the few companies offering a unified AI platform for UDP capable of processing even the most complex documents.

How to implement document data extraction

Choosing the Right Solution

When it comes to choosing an automated data extraction solution, there are a few key features you should keep in mind to ensure that your business is able to fully benefit from process automation.

  • Multiple Data Format Support: An automated data extraction solution should be able to handle various types of documents such as PDF, TXT, DOC, DOCX, JSON, and XLX for streamlined data extraction.
  • Cross-Application Compatibility: The ability to export extracted data to commonly used business applications such as Tableau and ERP systems like SAP and Oracle is crucial for seamless integration with current business processes.
  • Data Quality Control: A customizable data validation feature allows for checks on missing, inaccurate, or invalid data to ensure the extracted information is of high quality.
  • User-Friendly Interface: A zero-code environment and intuitive interface allows for ease of use, even for employees without a high level of technical skills.
  • Advanced Data Processing: Additional functionalities such as data transformation capabilities, such as data filtering, cleaning and sorting, allows for further refinement of extracted data to add more value to it.

The right solutions will depend on the current and future use cases, data processing volume, and project priorities (quality cost, and speed).

If cost is the primary driver and the company has in-house software development resources, open-source solutions such as Tesseract would be a good fit. For organizations with in-house resources willing to spend a bit more, cloud OCRs (e.g., Form Recognizer, Document AI, or Textract) will provide better quality results.

Businesses wanting a user-friendly business solution that can address primarily structured documents (e.g. W9 forms) or low variability semi-structured documents (e.g., invoices) would benefit from an IDP solution.

For companies looking for a long-term partner capable of processing even the most complex document types such as contracts, bills of lading, and customs forms with handwritten notes, stamps, and signatures, a UDP platform from a vendor like super.AI may be the perfect fit. By opting for a more capable UDP solution users get the added benefit of processing other unstructured data types such as images, videos, and audio as use cases and data processing needs evole.

Try Before You Buy

With such a wide variety of solutions available and many promising more than they can deliver, it is essential to try before you buy. This involves running a Proof of Value (POV) pilot with the vendor. For the POV, one should select 100-200 representative document samples for a given sample and test the results from the vendor's platform.

Note that the automated extraction level depends on the document's complexity. For edge cases, you would still need humans-in-the-loop (HITL) to process/review low-confidence results. Make sure the selected vendor offers an acceptable HILT interface. Also, some vendors offer crowd-sourced resources for HITL processing. Consider if that is an acceptable solution for your needs or if you prefer hiring and maintaining an in-house HILT workforce.

Managing the Document Data Extraction Solution

Some Document Data Extraction solutions require continuous monitoring and tuning as the data evolves. Others offer a fully managed service that off-loads the ongoing monitoring and tuning and provides a single blended cost of processing the document with a combination of AI and humans. Make sure to accurately assess the total cost of ownership of a solution before making a decision.

It is also important to consider the level of support and maintenance offered by the vendor of the Document Data Extraction solution. This includes the availability of customer support, software updates, and bug fixes. Having a dedicated support team in place can greatly assist in the smooth operation of the solution and ensure that any issues are addressed in a timely manner.

Another important aspect to consider is the scalability of the solution. As the volume of data and business operations grow, the solution should be able to handle the increased workload without any negative impact on performance. This can be achieved by implementing load balancing and distributed processing techniques, which ensure that the solution can handle large amounts of data without any downtime.

The security of the data must also be considered in choosing and deploying a data extraction solution. It is crucial that the solution adheres to industry standards and regulations when it comes to data protection. This includes measures such as encryption, data backup, and disaster recovery. It is also important to have a robust access control mechanism in place that ensures that only authorized personnel have access to the data.

Finally, it is important to have a clear strategy in place for data governance and data quality. This includes having a clear understanding of the data, the data lineage, and the data ownership. It also includes having a clear understanding of the data quality rules and standards that need to be adhered to. This will ensure that the data is accurate, consistent and reliable, and that it can be used to make informed decisions.

Get started with document data extraction

Data extraction tools are a powerful and efficient way to collect, store, and analyze data from documents. Implementing document data extraction can provide organizations with cost savings, improved customer experience, better decision-making capabilities, and reduced risks. As the demand for automation and the importance of data continue to grow, these tools are poised to play a vital role in shaping the future of business. However, it's important to remember that these technologies are not magic solutions and require careful planning and collaboration between experts to effectively solve real-world problems. Businesses have a variety of options to choose from, including OCR, IDP, and UDP vendors, and the best solution will depend on the organization's specific needs, goals, and internal development capabilities. Regardless, the investment in automated data extraction is a wise one, as those who choose to do so will reap its benefits in the long term.

Document data extraction use cases

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