What is Intelligent Document Processing?

Written by Harsimran Singh Labana | Oct 29, 2025 1:25:53 PM

Key takeaways from the blog

AI/ML-driven IDP solutions convert large amounts of unstructured data into a structured and usable format.
It validates the extracted data and integrates it with the downstream systems to offer a complete data management solution.
Its AI/ML-led architecture, along with GenAI augmentation, transforms the document processing by 360 degrees to generate higher value.

As businesses stand on the cusp of transformation, migrating from paper-based and semi-paper-based manual work scenarios to fully autonomous workflows, AI-driven solutions are looked up to with renewed interest. With almost 90% of enterprise data being in free-flowing text or unstructured format, Intelligent Document Processing, an AI-driven template-free document extraction and automation solution, now acts as the proverbial Sorcerer’s Stone for tapping and converting unstructured data into a structured and usable format.

What is Intelligent Document Processing or IDP?

Intelligent Document Processing or IDP solutions are based on AI/ML models and GenAI frameworks and are leveraged for end-to-end automation of paper-driven workloads. The solutions intelligently classify documents, extract unstructured, semi-structured, and structured data, validate the extracted data based on pre-configured rules and algorithms, and finally integrate the validated data from different document types into the downstream systems.

Some AI-led solutions go a step further to use Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG), along with Generative AI, to tap the extracted data for replying to natural language prompts and generate comprehensive answers and summaries. IDP and these AI-led solutions offer a comprehensive data management solution for businesses that work with different manual and semi-automated solutions.

Today, IDP has evolved into a smart and indispensable ally for businesses. It leverages AI/ML models to understand the context and meaning of the data that is extracted. As a result, the IDP solution goes much beyond simple data extraction to provide human-like data comprehension, accurately tagging and classifying data, which is immediately leveraged using Business Intelligence and AI Content Assistants to deliver meaningful and actionable insights.

What is the technical architecture of the AI-driven IDP solution?

The AI-driven IDP solution takes a layered approach to document processing:

Document ingestion: The solution ingests documents in different formats, such as PDFs, documents, document images, etc., that arrive through different sources, including email, scanned image repositories, SFTP, different applications through APIs, etc.
Image enhancement: The IDP solution optimizes the ingested image to increase the data extraction accuracy. The image gets transformed into black and white, de-skewed, and de-speckled. It then splits the multi-page documents into individual units or single pages.
Machine-readable text generation: IDP leverages AI/ML models for recognizing the text characters from different fonts and text formats. It thus converts image-based text into machine-readable text.
Document classification: IDP uses specialized models to analyze the text layout and classify the documents into different categories, such as purchase orders, invoices, contracts, etc. This facilitates routing the documents to the right extraction queue for further processing.
Unstructured data extraction: The solution uses NLP to understand the location of different named entities in the unstructured data layout by understanding the context. It also leverages GenAI for summarization and specific question-and-answer (Q&A) tasks.
Data validation: The IDP solution is adept at validating the extracted data by cross-referencing with business rules and the existing data stored in repositories. It also compares extraction output from multiple extraction engines to generate highly accurate outcomes.
HITL functionality: IDP leverages AI/ML models for higher confidence levels of extraction. However, human-in-the-loop functionality can always be leveraged for eyeball verification and exception handling. The feedback loop thus continuously re-trains the AI/ML models.
Data integration: The IDP solution seamlessly integrates with downstream systems by using integration layers, such as APIs, RPA bots, CSV, JSON, XML files, etc. The downstream systems could range from ERPs, CRMs, and CMS systems to legacy systems.

What are the top 11 advantages of IDP solutions?

AI/ML-driven IDP solutions take document processing to a significantly new level than the earlier document processing solutions, such as Optical Character Recognition (OCR). Their top 11 advantages are:

Reduced operational costs: IDP results in significant cost reductions in document processing as compared to manual operations. In some cases, the costs are as low as <1 USD, thereby reducing costs by 80%.
High processing speed: The document processing automation improves straight-through processing without human intervention. Thereby, it accelerates turnaround and improves throughput from several days to hours and from hours to a few minutes.
High accuracy: The IDP solution improves data extraction accuracy and eliminates human error by leveraging document image optimization and rule-based validations. The resulting data quality is high, and it integrates directly with the downstream systems.
Scalability: IDP is mostly hosted on the cloud and dynamically scales to process massive paper-based workloads. It easily handles fluctuating volumes of data processing without requiring the hiring or training of additional human resources.
Improved customer experience: Faster document processing improves the process turnaround time. The high processing speed enhances customer experience as payments can be processed within a couple of days, and loan applications can be processed within hours.
High transparency: The digital document ingestion process creates an auditable trail for every document that is processed. Transparency and auditability enable businesses to meet audit requirements and eliminate compliance risks.
Actionable insights: Having extracted unstructured data and converted it into a structured format that integrates with downstream systems, business intelligence (BI) and AI-driven solutions leverage the data to generate actionable insights that were previously buried within layers of unstructured data.
Digital transformation: IDP brings paper-based workflows into the arena of digital processing and facilitates end-to-end automation along with technologies, such as AI-augmented robotic process automation (RPA) bots.
Human-like intelligence: The IDP solution brings human-like decision-making, context-awareness, and action together in the same hub while processing unstructured data and integrating it with the existing IT architecture.
Elimination of template dependency: As it leverages AI/ML models, IDP breaks free from the template dependency. It leverages AI to locate data entities and extract them based on context. It processes different document types at scale by using ontologies.
Continuous learning: The underlying AI/ML algorithms continuously learn with each exception handling through human intervention. The accuracy continuously increases with each batch that is processed.

What is the difference between OCR, IDP, and RPA?

OCR, IDP, and RPA are three distinct but complementary technologies that are instrumental in driving end-to-end automation. This automation synergy unlocks efficiency gains, generates structured data, and integrates it with the downstream systems to tap actionable intelligence. However, the main characteristics of these complementary technologies are:

OCR is a rules-based engine that converts unstructured text from document images into machine-readable text. However, it fails to recognize the textual meaning and context. It fails to recognize human handwriting or even classify the documents.
IDP is an AI/ML and GenAI-driven engine that converts the text transcribed by the OCR engine into comprehensible and structured data by understanding the meaning and context. It extracts, classifies, and validates the data, and leverages exception handling for improving the underlying AI/ML models.
RPA uses the transformed and structured data extracted by OCR and IDP to automate repetitive, rules-based tasks and execute end-to-end process automation. It is rules-driven and AI-augmented, delivering high-speed, error-free process automation while integrating enterprise-wide business systems and workflows.

This data-driven synergy first digitizes the text from the document image, processes the data in a context-sensitive framework, converts it into a structured format, and triggers a chain of further actions to automate complex scenarios. This synergy enables true business agility through end-to-end process automation by leveraging unstructured enterprise data to achieve straight-through processing with 90%+ accuracy.

How does IDP work and chart its journey across the unstructured data?

The IDP journey begins by leveraging unstructured data extracted by in-built OCR engines or existing external OCR engines. It then culminates in converting the data into a structured and comprehensible format and integrating it with downstream systems. The discreet steps are:

To begin with, the businesses receive the data through different channels, including email, fax, scanned documents, images, etc.
Next, the OCR engine(s) extract and convert the text into machine-readable text. Here, document images get pre-processed to improve the image quality and extraction accuracy.
Subsequently, the IDP engine makes contextual inferences and classifies the document. It also uses NLP to comprehend the textual meaning; for example, it infers that in a financial document, the word “bank” refers to a financial institution and not a riverbank.
The IDP engine leverages NLP for its human-like comprehension. It also enables the engine to identify the placement of different elements in the document and continuously adapt to the changing input.
Next, the IDP engine leverages its AI/ML models to analyze the visual elements and the text for accurate data capture. It enables the engine to identify different document types.
Finally, the IDP engine either creates an XML, CSV, or JSON file for integrating the data with the business system or uses RPA bots or APIs to integrate with the downstream systems.

IDP thus automates the time-consuming task of document data extraction so that the employees can focus on their core competencies and knowledge work.

Top 8 important IDP use cases

Supply Chain Management and Logistics: Transform chaos into clarity in dynamic supply chain and logistics environments by ingesting typed or handwritten documents into business systems, such as TMS, CRM, and ERP. More use cases>>
Manufacturing: Automate procurement and invoicing across the entire procure-to-pay value chain and eliminate process latency and extended lead times. Improve communication between different business divisions and minimize expenditure. More use cases>>
Professional Services: Leverage IDP and intelligent automation for summarizing lengthy documents and managing customer cases while preserving the entire audit trails and case history. More use cases>>
Healthcare: Ensure seamless data extraction and record maintenance of patient health-related information by using intelligent data processing and RPA. Integrate the patient statistics and healthcare parameters directly with the electronic health record (EHR) systems. More use cases>>
Insurance: Digitize, review, and auto-categorize documents and hard copies received as claims by using IDP. Store them digitally for further processing. Automate the claims handling and management by using RPA. More use cases>>
Banking: Auto-extract reports from various modules such as "Execute Single data mart extraction", "Trade Query", "Account Balances", "Journal of Entries", and "P&L User Definable notepad". Improve financial administration and reporting with IDP and RPA. More use cases>>
Financial Services: Auto-collate new customer records, gather relevant data from different sources, and auto-report the onboarding customers to regulators with IDP and intelligent automation. More use cases>>
Miscellaneous: Ingest critical but unstructured data from different forms, reports, and handwritten notes in document-intensive industries. Process any document type without using templates and trigger the downstream automation process with IDP and RPA. More use cases>>

The evolution of document processing to IDP

Document processing has undergone significant evolution over the past few decades. From manual processing to OCR and from IDP to futuristic solutions on the horizon, document processing is continuously evolving.

Yesterday: Manual data processing was both costly and error-prone. The emergence of rule-based OCR set the pace for digital transformation. However, it lacked contextual understanding and awareness of the meaning of the extracted data. Additionally, the data extraction quality was highly dependent on the scan quality of the document image. Similarly, unrecognizable fonts and complex document layouts, such as tables and multiple columns, created document extraction accuracy challenges. It required significant updates to handle new document types.
Today: The current IDP version leverages inbuilt and external OCR engines, AI/ML models, and NLP for data extraction. The underlying AI/ML algorithms enable IDP engines to understand the context and meaning, enabling them to classify the documents. This framework revolutionizes how users manage huge amounts of large and complex documents in an unstructured format. Today, AI/ML has transformed IDP from a simple transcription solution to a cognitive automation solution that comprehends the text much like a human.
Tomorrow: The integration of AI/ML-driven IDP and GenAI is transforming document processing by 360 degrees to generate higher value. New features, such as context-sensitive search and customization, offer immense flexibility in integrating IDP as a tool in the wider application infrastructure to deliver end-to-end automation solutions, such as business case management.

Document processing has evolved from manual and rules-based processing to a skilled data extraction mechanism by using sophisticated AI/ML models. The human-like cognizance that the modern-day IDP possesses allows business users to integrate it as a module in complex automation solutions.

Simply put

AI/ML-driven IDP converts unstructured data into a structured and usable format. Its smart cognitive characteristics impart a human-like cognizance and decision-making to data extraction from documents in unstructured and semi-structured formats. As a result, IDP, along with RPA and sophisticated AI models, brings forth smart usable solutions with fast and accurate throughput for different industries. With the continuous emergence of different high-end AI models, IDP is evolving further to deliver high-end automation solutions.

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