Intelligent Document Processing (IDP) is changing the way businesses handle documents. From invoices and contracts to employee forms, manual processing is time-consuming and prone to errors. An IDC study found that inefficient document handling can lead to a 30% drop in productivity, making automation a critical need for modern businesses.
IDP leverages AI, machine learning, and natural language processing to extract, classify, and analyze data with speed and accuracy. According to a report by Docsumo, advancements in AI now allow businesses to automate entire document workflows, adapting to different formats, layouts, and templates without manual intervention.
By reducing manual effort, IDP helps businesses cut costs, minimize errors, and improve overall efficiency. In today’s blog, we’ll explore how Intelligent Document Processing (IDP) is transforming businesses by automating document workflows, reducing costs, and improving efficiency.
What is Intelligent Document Processing (IDP)?
Intelligent Document Processing (IDP) is an artificial intelligence-based approach to automate the extraction, classification and validation of data from structured, semi-structured and unstructured documents. It uses technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), and deep learning to handle invoices, contracts, forms, and other documents with very little human involvement.
IDP not only captures text but also understands context, detects key insights, and integrates with business systems to enhance accuracy, efficiency, and compliance in document-heavy workflows.
How Intelligent Document Processing (IDP) Works
1. Data Ingestion and Preprocessing
- Data Capture: IDP starts by capturing data from multiple sources, such as paper documents, PDFs, emails, and images.
- Preprocessing: It involves approaches such as binarization, noise reduction, de-skewing, and cropping. These improve document quality so that extracted data is as accurate as possible. Certain systems can be controlled for human-in-the-loop (HITL) interventions for jobs such as data labeling and annotation.
2. Document Classification
- Categorization: Documents are assigned a predefined categorization based on their type and content, such as invoices, contracts, and purchase orders.
- Technologies used: AI algorithms with NLP and OCR to detect the result patterns and classify the documents automatically. Some advanced systems may use supervised or unsupervised learning for classification.
3. Data Extraction
- Extraction Process: Relevant data fields such as names, dates, amounts, or policy numbers are extracted using OCR, NLP, and deep learning models.
- Handling Complexity: IDP systems can process complex layouts by identifying key-value pairs or extracting tables from documents. Intelligent Character Recognition (ICR) enhances OCR capabilities for difficult-to-read text.
4. Data Validation
- Accuracy Checks: Extracted data is validated against internal databases or external sources to ensure accuracy.
- Human Oversight: Outliers or errors are flagged for manual review to improve model performance over time through feedback loops.
5. Data Integration
- System Integration: Validated data is seamlessly integrated into downstream systems such as ERP platforms, customer service tools, or robotic process automation (RPA) workflows.
- End Goal: This step ensures that the processed data is actionable and ready for decision-making or further automation.
Best Intelligent Document Processing Solutions
UiPath offers a comprehensive automation platform that integrates RPA with AI capabilities. Its Document Understanding feature enables users to extract and interpret data from diverse documents, including invoices and receipts, supporting various formats and layouts.
ABBYY specializes in content intelligence solutions, providing tools like FlexiCapture and Vantage for intelligent document processing. These platforms utilize AI to extract data from structured and unstructured documents, facilitating automation in document-centric processes.
Developed by John Snow Labs, Spark NLP is an open-source library that delivers advanced natural language processing capabilities. Built on Apache Spark, it offers pre-trained models and pipelines for tasks such as named entity recognition and sentiment analysis, enhancing the processing of unstructured text data.
Executive Technologies offers SearchExpress, a document management solution that incorporates AI to automate data entry and workflow processes. It supports document capture, indexing, and retrieval, aiming to create a paperless office environment.
Developed by Oak Ridge National Laboratory, Piranha is a text mining system designed to process free-text documents and reveal relationships among them. It employs AI to analyze large document sets, presenting results in clusters of prioritized relevance, useful across domains like healthcare and national security.
Adobe has integrated AI capabilities into its Acrobat platform, introducing features like an AI assistant that allows users to interact with PDFs through real-time conversations. This tool can summarize content, clarify terms, and provide definitions, enhancing the user experience in handling complex documents.
Part of Microsoft’s suite of AI solutions, Syntex enhances content understanding and automates content processing within Microsoft 365. It leverages AI to extract information from documents, classify content, and integrate seamlessly with existing workflows.
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Simplify Document Processing with Kanerika’s FLIP: AI-powered DataOps Platform
In the realm of Intelligent Document Processing (IDP), FLIP by Kanerika stands out as a transformative solution. This AI-powered, low-code/no-code DataOps platform simplifies and automates data transformation pipelines, enabling businesses to extract valuable insights more efficiently.
Key Features of FLIP:
- AI-Powered Document Processing: FLIP utilizes advanced AI and Optical Character Recognition (OCR) technologies to accurately extract data from various formats, including PDFs, JPEGs, and scanned documents. This ensures swift, error-free processing across diverse formats, enhancing efficiency and reducing manual intervention.
- Seamless Integration: The platform offers pre-built connectors for various data sources and ERP systems, facilitating real-time data synchronization and validation. This integration capability ensures a smooth and consistent workflow across diverse invoice sources.
- Real-Time Automation: FLIP provides pipeline monitoring with immediate insights, automated critical alerts, and proactive file monitoring for missing or delayed files. This real-time processing and automation grant crucial insights into data operations.
- Intelligent Reconciliation: The platform automatically identifies and flags discrepancies, performs real-time validation checks for duplicates, inconsistencies, and missing fields, and continuously monitors processing accuracy with detailed audit trails.
Real-World Application:
A notable example of FLIP’s impact is its collaboration with KBR Inc., a global leader in science, technology, and engineering solutions. FLIP automated the extraction of data from diverse document types, including PDFs and Excel sheets, converting them into structured, ready-to-use formats that integrated seamlessly with KBR’s systems. This automation eliminated manual intervention, accelerated client onboarding, enhanced data accuracy, and enabled KBR’s teams to make faster, data-driven decisions.
Key Technologies Powering Intelligent Document Processing
1. Optical Character Recognition (OCR)
OCR (Optical Character Recognition) will provide machine-readable text from scanned images, PDFs, or handwritten documents. This is the first step toward digitizing paper-based content.
Advanced OCR: Intelligent Character Recognition (ICR) takes OCR a step further and can be used to read hard-to-read text, e.g., handwriting or special fonts. For example, A logistics company employs OCR to automatically scan shipping labels and extract tracking numbers, eliminating manual data entry errors.
2. Natural Language Processing (NLP)
NLP enables systems to understand the context and meaning of text in documents. Such tools assist in document classification and relevant field extraction.
For instance, applying NLP to contracts can help a legal firm swiftly extract clauses and group them by category, thereby speeding up compliance check
3. Machine Learning (ML)
ML models learn from data to improve document classification and data extraction over time. They adapt to new document formats and layouts with minimal human intervention.
Example: An insurance company uses ML to process claims forms, identifying policy numbers and claim details across varying formats.
4. Computer Vision
Computer vision analyzes visual elements in documents, such as tables, graphs, or signatures. It enhances OCR by recognizing non-textual elements.
Example: A healthcare provider uses computer vision to extract patient information from medical charts that include handwritten notes and diagram.
5. Robotic Process Automation (RPA)
RPA automates repetitive tasks like routing extracted data into enterprise systems or generating reports.
Example: In finance, RPA integrates extracted invoice data into accounting software for automated payment processing.
6. Generative AI and Large Language Models (LLMs)
These advanced AI technologies expand IDP’s capabilities by handling complex document types and improving contextual understanding.
Example: A customer service team uses generative AI to summarize lengthy complaint letters into actionable insights for faster resolution.
IDP vs ADP: What is the Difference?
| Feature | Intelligent Document Processing (IDP) | Automated Document Processing (ADP) |
| Definition | Uses AI, machine learning, and NLP to extract, classify, and validate data from documents. | Relies on rule-based automation and OCR to process structured documents. |
| Technology Used | AI, NLP, OCR, deep learning, computer vision, machine learning. | OCR, rule-based workflows, template-based automation. |
| Document Type Handling | Processes structured, semi-structured, and unstructured documents. | Works best with structured and some semi-structured documents. |
| Flexibility & Adaptability | Learns and adapts over time, improving accuracy with more data. | Requires predefined rules and templates, making it rigid. |
| Accuracy & Intelligence | Understands context, extracts insights, and automates decision-making. | Extracts text but lacks contextual understanding. |
| Examples | IDP extracts key information from contracts, emails, invoices, and legal documents while improving accuracy over time. | ADP automates invoice data entry using fixed templates but struggles with document variations. |
| Best Use Cases | Financial services, healthcare, legal, insurance, supply chain (complex document workflows). | Invoice processing, standardized forms, basic document digitization. |
Top 6 Benefits of Intelligent Document Processing (IDP)
1. Increased Efficiency
- Automates the processing of documents, as well as decreases data entry and review by human experts.
- Accelerates workflows by extracting, classifying, and validating data in real time.
2. Higher Accuracy & Reduced Errors
- Higher precision & diminished errors IDP leverages AI and machine learning to reduce errors in data extraction and processing.
- Provides uniformity across various document types, minimizing inconsistencies.
3. Cost Savings
- Reduces operational costs by automating document handling and verification.
- Lowers costs related to rework, compliance fines, and productivity loss.
4. Scalability & Flexibility
- No predefined template is required for processing structured, semi-structured, and unstructured documents.
- It scales with your organization’s growing document amounts and changing business requirements, all without the need to hire more people.
5. Government Security & Compliance
- It Keeps correct and easy-to-verify records for industry regulations and legal compliance.
- It Safeguards sensitive information using AI-powered document security, minimizing breach risks.
6. Seamless Integration with Enterprise Systems
- Integrates with ERP, CRM, and RPA tools for seamless end-to-end automation
- Processed documents are made available for real-time insight into decisions and relaying business intelligence.
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Use Cases of Intelligent Document Processing (IDP) Across Industries
1. Banking and Financial Services
- Loan Applications: Automates the processing of loan applications by extracting and validating data from documents like income proofs, bank statements, and utility bills. For example, during the pandemic, banks in Spain used IDP to process over 20 million pages of loan documents with 75% automation and 99% accuracy.
- KYC Compliance: IDP simplifies Know-Your-Customer (KYC) processes by extracting data from identity documents such as proof of residence or income.
- Expense Management: Automates invoice processing and financial reporting by extracting data from receipts and expense forms.
2. Healthcare
- Patient Records Management: IDP digitizes patient records, extracts data from medical forms, and organizes them for easy retrieval. For example, the U.S. FDA used IDP to process adverse drug event forms with 99% accuracy
- Claims Processing: Automates healthcare claims by extracting critical data from medical forms to expedite reimbursements
3. Insurance
- Claims Automation: Processes claims documents automatically by extracting policy numbers and license plates, reducing manual effort. For instance, Ecclesia Group used IDP to streamline customer correspondence management.
- Fraud Detection: Enhances fraud detection by analyzing patterns in claims submissions and policy documents
4. Logistics and Transportation
- Shipping Documentation: Automates the handling of customs declarations, bills of lading, delivery receipts, and driver logs to reduce errors and delays. DHL achieved a 70% efficiency increase by using IDP to process invoices in multiple languages
- Supply Chain Management: Streamlines operations by processing barcodes, manifests, and invoices for rapid supply chain workflows
5. Legal
- Contract Analysis: Uses Natural Language Processing (NLP) to extract clauses and obligations from contracts for faster review. Legal teams can also analyze court records to build stronger cases.
- Compliance Reporting: Automates regulatory reporting by extracting relevant data from legal documents to ensure adherence to standards.
6. Human Resources
- Employee Onboarding: Accelerates onboarding by automatically extracting data from resumes, job applications, and HR forms.
- Document Management: Organizes employee records for compliance with retention policies while ensuring easy retrieval.
7. Manufacturing
- Invoice Processing: Automates supplier invoice processing for faster payments.
- Quality Control Documentation: Extracts data from inspection reports to ensure compliance with quality standards.
8. Retail and E-Commerce
- Customer Onboarding: Simplifies account creation by extracting data from identity documents.
- Order Management: Automates order processing by capturing details from purchase orders and invoices.
What Factors to Consider While Choosing IDP Solution
A robust IDP platform should offer the following capabilities:
1. Artificial Intelligence and Machine Learning (AI/ML) Capabilities
- Advanced Machine Learning (ML) algorithms should be able to improve over time by learning from document patterns.
- Natural Language Processing (NLP) helps understand unstructured data, ensuring better context recognition and classification.
- AI-based solutions should support automated data validation and correction, reducing the need for human intervention.
2. Optical Character Recognition (OCR) for High Accuracy
- OCR technology is essential for extracting text from scanned documents, PDFs, or handwritten notes.
- Look for solutions with intelligent OCR that can handle poor-quality scans, distorted text, or different font styles.
- Some IDP platforms offer AI-enhanced OCR, which improves extraction accuracy beyond traditional OCR methods.
3. Data Classification and Extraction
- The solution should automatically categorize, and tag documents based on content and format.
- It must be able to extract key fields such as invoice numbers, customer names, or contract dates from various document types.
- Intelligent classification ensures that structured and unstructured data can be processed without extensive manual configuration.
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4. Automated Data Validation and Error Handling
- The IDP solution should cross-check extracted data with existing databases, predefined rules, or external APIs to detect and correct errors.
- A good system will flag inconsistencies and provide confidence scores for extracted data, helping users prioritize review tasks.
5. Seamless Integration with Enterprise Applications
The IDP system should integrate with:
- Enterprise Resource Planning (ERP) systems like SAP, Oracle, or Microsoft Dynamics.
- Customer Relationship Management (CRM) platforms such as Salesforce or HubSpot.
- Document Management Systems (DMS) like SharePoint or Google Drive.
- Robotic Process Automation (RPA) tools like UiPath or Automation Anywhere to enable end-to-end automation.
6. Security and Compliance
- The system should comply with industry standards such as GDPR, HIPAA, ISO 27001, and SOC 2 for secure document handling.
- End-to-end encryption, role-based access controls (RBAC), and audit logs ensure that sensitive data is protected.
- Cloud-based solutions should provide options for on-premise or hybrid deployments to meet specific regulatory requirements.
What Factors to Consider When Evaluating IDP Vendors
1. Scalability and Performance
- Can the solution process large volumes of documents without performance degradation?
- Does it support batch processing and real-time document ingestion?
- Can it handle different document types such as invoices, contracts, handwritten notes, and emails?
2. Ease of Use and Customization
- The solution should have an intuitive user interface with a minimal learning curve.
- Some platforms offer drag-and-drop workflows for setting up document automation rules.
- Customization options should be available for defining business-specific templates and data extraction rules.
3. Support for Multiple Languages
- If your business operates globally, ensure the IDP solution supports multilingual document processing.
- Advanced NLP models should be able to process documents in various languages without significant accuracy loss.
4. Customer Support and Vendor Reliability
- A reliable vendor should provide 24/7 customer support, documentation, and training resources.
- Check for client reviews, case studies, and references to understand real-world performance.
- Evaluate the vendor’s product roadmap and innovation cycle to ensure future improvements.
5. Total Cost of Ownership (TCO)
- Consider initial setup costs, licensing fees, training expenses, and ongoing maintenance costs.
- Cloud-based solutions generally offer pay-as-you-go pricing, while on-premises systems involve one-time investment and infrastructure costs.
- Check for hidden fees related to API usage, data storage, or advanced feature access.
Kanerika is a leading AI-driven technology company specializing in Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions that help businesses optimize operations, automate workflows, and drive intelligent decision-making. Our expertise in AI-powered automation enables organizations to process vast amounts of data efficiently, improve accuracy, and unlock new growth opportunities.
With a track record of successful AI/ML implementations across industries like finance, healthcare, logistics, and retail, we help businesses harness the power of data to reduce costs, enhance productivity, and streamline complex processes. From intelligent automation to real-time insights, our solutions are designed to increase efficiency and accelerate innovation.
At Kanerika, we partner with businesses to build custom AI solutions that address unique challenges and deliver measurable impact. Whether you’re looking to integrate AI for automation, improve predictive analytics, or enhance decision-making with machine learning models, our team of experts is here to help.
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