What is AI Document Processing & How to Implement It?

Document Automation Tips

With AI document processing, you can automate data extraction, classification, and management, transforming a tedious task into a streamlined process.

October 28, 2024

What is AI Document Processing & How to Implement It?

Introduction

“Artificial intelligence will reach human levels by around 2029.” –Ray Kurzweil, a computer scientist, inventor, author, and futurist.

Just as Ray had predicted, businesses are using Intelligent Document Processing (IDP) to handle data with near-human precision. IDP automates tasks like data extraction, classification, and analysis and bridges the gap between human insight and machine efficiency.

In this article, we'll cover what Intelligent Document Processing (IDP) is, how it works for business process automation, its benefits, and tips on choosing the right IDP tool for your business.

What is Intelligent Document Processing?

Intelligent Document Processing (IDP) is a technology that uses artificial intelligence (AI) and machine learning (ML) to automate the extraction, processing, and analysis of data from various types of documents like invoices, contracts, receipts, emails, and forms.

Unlike traditional Optical Character Recognition (OCR), IDP incorporates advanced capabilities of AI, ML, and NLP to recognize text and understand the document’s content, context, and structure, providing more accurate and comprehensive data interpretation.

IDP enhances operational efficiency, resulting in more accurate data extraction, reduced manual effort, and improved handling of diverse and complex document types. This technology simplifies workflows and provides valuable insights from data, making it essential for organizations aiming to improve productivity and decision-making.

7 Key differences between IDP and OCR?

OCR (Optical Character Recognition)

What it does

Converts documents into editable and searchable text.

How it works

Recognizes text from scanned images and turns it into digital text.

Accuracy

High for simple, well-structured documents; drops with poor quality or complex formats.

Scalability

Easily scalable for handling increased volumes, especially with cloud-based solutions.

Template requirement

Requires templates to operate, which can be costly and difficult to maintain.

Learning capability

Does not improve over time; it requires manual adjustments.

Use cases

Suitable for basic tasks like digitizing documents, IDs, or converting books.

IDP (Intelligent Document Processing)

What it does

Automates the extraction and analysis of data using AI, NLP, and ML.

How it works

Uses AI and ML to extract, process, and understand data from various document types.

Accuracy

Very accurate for structured and semi-structured documents; improves with unstructured data over time.

Scalability

Highly scalable, ideal for large document volumes and cloud-based environments.

Template requirement

Works without rigid templates, adapting to different document formats.

Learning capability

Uses machine learning to continuously enhance accuracy and efficiency.

Use cases

Ideal for automating complex business processes like invoice processing, claims management, and customer onboarding.

Let’s take a closer look at the technology behind Intelligent Document Processing and how it powers advanced automation.

What are the technologies used in intelligent document processing?

Here are the top five technologies used in Intelligent Document Processing:

1. Optical Character Recognition (OCR)

Converts scanned documents, PDFs, or images into editable and searchable text.
Recognizes characters and symbols,
Analyzes document layouts,
Interpret both printed and handwritten text.

2. Natural Language Processing (NLP)

Focuses on the interaction between computers and humans through natural language.
Analyzes text for syntax and semantics.
Performs entity recognition.
Conducts sentiment analysis to enable machines to understand and interpret human language.

3. Machine Learning (ML)

Enables IDP systems to learn from data and improve over time.
Trains algorithms to identify patterns.
Classifies documents and detects anomalies.
Enhances data extraction and processing accuracy.

4. Deep Learning

Utilizes neural networks with multiple layers to model complex patterns in data.
Automated feature extraction from digital documents.
Enhances image and text recognition.
Improves natural language understanding for sophisticated unstructured data processing.

5. Robotic Process Automation (RPA)

Automates repetitive tasks using software robots.
Reduces manual effort.
Improves accuracy in document handling.

The global intelligent process automation size, as per revenue, is expected to reach over $25.9 billion in 2027, growing at a CAGR of 13.8% for the period 2022-2027, with its main drivers being RPA, ML, AI, and cognitive learning technologies. These technologies work together to enable Intelligent Document Processing, streamlining document-heavy complex tasks like data entry, document classification, and data extraction.

How accurate is intelligent document processing?

Implementing Intelligent Document Processing can reduce errors by over 52% and achieve an accuracy rate of 99%, significantly reducing the risk of critical errors commonly associated with manual document handling.

The accuracy of IDP systems is influenced by several factors, such as:

Document Quality: High-quality documents enhance extraction accuracy, while poor image quality can hinder performance.
Continuous Learning: IDP systems improve over time through machine learning, adapting to new document formats and layouts based on previous data.
Human Oversight: Some systems incorporate human-in-the-loop mechanisms to handle exceptions, further boosting accuracy.

IDP represents a significant advancement over traditional document processing methods, providing organizations with reliable data extraction capabilities that support better decision-making and operational efficiency.

How did IDP come into existence? Evolution of intelligent document processing.

The evolution of IDP is a remarkable journey marked by significant technological advancements.

Historical Background

The roots of IDP trace back to Optical Character Recognition (OCR) technology, which emerged in the early 20th century. In 1914, Emanuel Goldberg invented a machine capable of reading characters and converting them into telegraph code, laying the groundwork for future developments in document processing.

Intelligent Document Processing has changed how organizations manage documents. As the amount of data grew, OCR’s limitations became clear, leading to the creation of IDP.

From OCR to IDP

To improve efficiency, IDP was developed, allowing organizations to automate over 80% of document tasks, reducing the need for human work.

Handling Unstructured Data

IDP can process unstructured data, which makes up a large part of company information. It uses natural language processing (NLP) to understand and pull relevant details from a variety of document types, including handwritten notes and images.

Expanding Use Cases

IDP is used in a wide variety of industries for tasks such as invoice processing and compliance checks. It identifies and categorizes documents according to well-defined rules, which are established by business analysts and subject matter experts.

Future Trends

In the future, IDP will expand as document types grow more complex, incorporating multi-format integration for processing audio and video content alongside traditional documents. This advancement will enhance business process automation through data extraction and analysis for managing claims and reports.

As per the report by MarketsandMarkets, “The global Business Process Automation Market size is projected to reach $19.6 billion by 2026, growing at a CAGR of 12.2% during the forecast period, 2022-2026.” This projection clearly underlines the need for businesses to adopt intelligent document processing, gaining competitive advantage and streamlining business operations.

How does intelligent document processing work?

Understanding how Intelligent Document Processing works is crucial for enhancing efficiency, reducing costs like operational costs or labor costs, and ensuring compliance, allowing organizations to automate workflows effectively.

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1. Document ingestion

The first step in IDP involves gathering documents from multiple sources to create a centralized repository for intelligent automation. This ensures that all relevant documents, whether physical or digital, are captured and ready for processing.

Scanned Images: Physical documents, such as paper documents, are digitized using scanners. High-resolution scans ensure that even fine details are captured accurately, providing a clear base for data extraction.
Emails: Documents attached to emails are automatically detected and extracted. The system can process various attachment formats, including PDFs, images, and text files, allowing seamless integration of incoming information.
Digital Forms: Data from online forms and electronic submissions is directly captured. This includes forms filled out on websites or mobile applications, which are often structured for easy extraction.
Cloud Storage: Documents stored in cloud services like Google Drive, Dropbox, or OneDrive are retrieved using API integrations. This ensures that data from various digital repositories is accessible, centralizing document management.

2. Pre-processing

Pre-processing enhances the quality and format of the documents to prepare them for accurate data extraction. This step is crucial for improving OCR results and ensuring the data is machine-readable.

Image Enhancement: Techniques such as de-skewing (correcting tilted text), de-noising (removing background noise), and contrast adjustment improve the readability of scanned documents. Enhanced images lead to more accurate OCR results.
Optical Character Recognition (OCR) Conversion: Documents are converted into a machine-readable format. For instance, a scanned PDF is transformed into text that can be processed by software applications, making the information searchable and editable.

3. Classification

Documents are sorted into categories based on their type and content, enabling the application of specific processing rules for each category. This classification is essential for managing diverse document types effectively.

Machine learning models: AI algorithms analyze the content and structure of documents to classify them into predefined categories, such as invoices, contracts, receipts, or correspondence. This classification is crucial for applying the right processing rules to each document type.
Template matching: The system compares documents to existing templates to determine their type. This is particularly useful for documents with a consistent structure, such as standard forms, enabling quick and accurate categorization.

4. Data extraction

Key data points are identified and extracted from the documents, transforming unstructured data into structured information. This step leverages advanced technologies to ensure the accuracy and relevance of the extracted data.

Optical Character Recognition (OCR): OCR technology reads printed or handwritten text from documents and converts it into digital text. This is essential for extracting text from scanned images, turning them into usable data.
Natural Language Processing (NLP): NLP algorithms analyze the text to understand context and semantics, allowing the system to accurately extract relevant information, such as dates, names, amounts, and addresses. This step ensures that the extracted data is meaningful and contextually appropriate.
Pattern recognition: The system uses predefined patterns (e.g., date formats, invoice numbers) to locate and extract specific data points from the text. This method enhances the precision of data extraction, especially for standard data fields.

5. Validation and verification

The extracted data is checked for accuracy and consistency to meet quality standards. This step involves cross-referencing data with existing records and applying business rules.

Cross-referencing: Data is validated by comparing it with existing databases or records. For example, an extracted invoice number can be checked against the company's accounting system to ensure it matches an expected format, verifying its authenticity.
Business rules application: Specific rules are applied to the data to ensure it meets predefined criteria. For example, dates must be within a certain range, or amounts must be above a minimum threshold, ensuring the data complies with business standards and requirements.

6. Post-processing and integration

The final step involves formatting the data and integrating it into business systems to enable seamless workflows. This step ensures the processed data is ready for further use and automation.

Data formatting: The extracted data is transformed into the required format for further use. For example, converting dates to a standard format or structuring extracted information into a table ensures compatibility with other systems.
System integration: The formatted data is imported into enterprise systems such as ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), or databases. This ensures seamless workflow continuation and data availability across the organization, promoting efficiency and accuracy.
Workflow automation: Based on the processed data, automated processes are triggered. For instance, an approved invoice might automatically initiate a payment process in the financial system, reducing manual intervention and speeding up operations.

By delving into each step of intelligent document processing, you can get a clear understanding of how the technology works and how to implement it effectively. This knowledge enables your organization to optimize your document workflows, resulting in enhanced productivity and operational efficiency.

For instance, Docxster streamlines document processing through this multi-step approach. First, it captures documents from various formats and automatically classifies them based on content. Next, it uses advanced NLP to analyze and extract relevant data, ensuring accuracy through machine learning and OCR. Finally, it provides analytics for workflow optimization, enhancing efficiency and decision-making.

Key benefits of intelligent document processing

Let’s look at a few key benefits of Intelligent Document Processing across various industries:

1. Banking and Finance

Intelligent document processing can save finance departments 25,000 hours of rework and $878,000 annually due to human errors for organizations with 40 full-time accounting staff, according to Gartner.

Benefits of IDP in banking and finance:

Faster Loan Processing: Automates the extraction of data from loan applications, reducing manual processing efforts and speeding up approval times.
Fraud Detection: Identifies discrepancies and potential fraud by quickly analyzing large volumes of documents like bank statements and invoices.
Regulatory Compliance: Ensures that data from legal documents is captured accurately, aiding in compliance with financial regulations such as KYC (Know Your Customer) and AML (Anti-Money Laundering).

2. Healthcare

Benefits of IDP in healthcare industry:

Patient Data Management: Automatically extracts and processes medical records, test results, and patient forms, leading to improved patient care and faster decision-making.
Claims Processing: Streamlines the insurance claims process by automating the review of medical documents and coding, resulting in faster reimbursements.
Regulatory Compliance: Ensures that patient data is accurately handled and stored in compliance with healthcare regulations like HIPAA.

3. Insurance

Benefits of IDP in insurance industry:

Claims Automation: Reduces manual intervention in claims processing by extracting data from claim forms, policy documents, and supporting files such as photos or accident reports.
Risk Assessment: Automates processing documents related to risk assessments, underwriting, and policy renewals.
Customer Experience: Enhances customer service by quickly handling documentation during claims, policy issuance, and renewals.

4. Manufacturing

Benefits of IDP in manufacturing industry:

Supply Chain Management: Automates the processing of purchase orders, invoices, and delivery notes, ensuring smoother supply chain operations and better vendor management.
Quality Control: Speeds up documentation of quality control checks, production reports, and compliance certifications, ensuring timely approvals and process improvements.
Inventory Management: IDP assists in digitizing and processing documents related to inventory, improving tracking and stock management.

5. Accounting and Finance

In December 2022, in Nakano-ku, Japan, the government reduced its work volume by 30% and outsourcing costs by 25% by using IDP solutions to transform its resident tax collection process.

Benefits of IDP in accounting and finance:

Invoice Processing: Automates invoice data capture, reducing the time and errors associated with manual data entry in accounting and financial services.
Audit Readiness: Automatically organizes and processes financial statements, tax documents, and receipts, making audit preparation faster and more accurate.
Financial Reporting: Streamlines the extraction and processing of financial data, ensuring timely and accurate reporting.

6. Human Resources

Benefits of IDP in Human Resources:

Employee Onboarding: Automates the processing of onboarding documents like contracts, tax forms, and identification documents, improving the efficiency of the hiring process.
Document Management: Organizes and stores employee records, performance evaluations, and payroll documents, ensuring compliance and easy retrieval.
Compliance and Reporting: Helps ensure that HR documents meet labor law requirements and anssists with automated reporting for HR analytics.

Each of these sectors benefits from IDP through reduced manual effort, faster processing times, and improved accuracy, ultimately leading to enhanced productivity and customer satisfaction.

Conclusion

As companies explore automation solutions, investing in an IDP system could be a strategic move that positions them for success with digital transformation.

Embracing IDP streamlines operations and empowers organizations to confidently make data-driven decisions, ultimately leading to better customer experiences and a stronger competitive advantage. The transition to IDP is not just about adopting new technology; it's about reimagining the potential of organizational workflows and unlocking new opportunities for innovation and efficiency.
Looking for an ideal IDP solution? Docxster is here. Try Docxster for free and see how it can streamline your workflow and amplify your growth!