Intelligent Document Processing (IDP) is an AI-powered document processing technique that not just scans and captures structured, unstructured and semi-structured data, but also understands it deeply. It is a modern development in the realm of document processing, a field that has been evolving since the early 1900s with the advent of document OCR (Optical Character Recognition).

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Intelligent Document Processing (IDP)
leewayhertz.com/intelligent-document-processing-idp
Inbound
Documents
Received
Prepare
Documents
Extract Data
Human in the Loop
Validate, Verify &
Enrich
Deliver to End
System
Internal & External
Systems
Action Triggered
Understand
Meaning,
Intent &
Document Type
LeewayHertz
In the dynamic and data-centric landscape of modern business, documents serve as an
essential channel through which information, ideas, and fuel for decision-making flow.
However, traditional document processing methods have proven to be a bottleneck in the
race for efficiency and accuracy. The labor-intensive, error-prone nature of manual data
entry, coupled with the vast amount of unstructured data in various formats such as business
documents, emails, images, and PDFs, has necessitated a paradigm shift.
Welcome to the world of Intelligent Document Processing (IDP) – the new-age solution that
harnesses the power of artificial intelligence technologies like Natural Language Processing
(NLP), computer vision, deep learning and Machine Learning (ML) to simplify document
management. IDP automates the extraction, processing and analysis of data from an array
of documents, eliminating the need for manual data entry, reducing errors and significantly
boosting efficiency.
In the information age, where data is the new currency, approximately 80% of a company’s
data remains unstructured, residing in texts scattered across documents of various forms.
This poses a major challenge, requiring substantial time and resources to collate and make
sense of it. But with IDP, businesses can tap into this unstructured data reservoir, extracting
valuable insights that can drive strategic decision-making.

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Across industries, from finance to healthcare and government to education, IDP is making its
mark, automating document-intensive tasks like invoice processing, contract management,
compliance reporting and more. It goes beyond automation by providing businesses with
tools to extract strategic value from their unstructured data.
Today, the global market for intelligent document processing, valued at over $1 billion in
2021, is projected to reach upwards of $6 billion by 2027, according to Straits Research. This
indicates the growing recognition of IDP as an essential driver of digital transformation.
This article dives into the world of IDP, demystifying its working, exploring its applications and
showcasing its transformative potential. Discover how your business can leverage IDP to
optimize operations and unlock unprecedented growth.
What is Intelligent Document Processing (IDP)?
What can IDP do?
How IDP works: The detailed workflow
The key components of intelligent document processing
The role of AI and ML in intelligent document processing
Use cases of IDP
The technology stack of IDP
Benefits of intelligent document processing
Implementing intelligent document processing
Future Trends in intelligent document processing
What is Intelligent Document Processing (IDP)?
Intelligent Document Processing (IDP) is an AI-powered document processing technique
that not just scans and captures structured, unstructured and semi-structured data, but also
understands it deeply. It is a modern development in the realm of document processing, a
field that has been evolving since the early 1900s with the advent of document OCR (Optical
Character Recognition). The progress in technologies like machine learning, natural
language processing, and computer vision has reached a level where they can be effectively
employed in tasks such as classifying documents and extracting data. IDP leverages these
AI technologies to automate and enhance document-related processes.
IDP stands out from conventional document processing due to its unique capabilities. It goes
beyond mere recognition of words and characters, but rather interprets the meaning and
context of the data. Thus, IDP does more than just capturing data – it provides valuable
business insights and continuously enhances its performance by learning, which lessens the
necessity for human involvement.

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To understand the concept, let’s say you have a pile of letters that includes utility bills,
personal letters, promotional flyers, and so on. Now, if you were to manually sort them, you’d
have to open each envelope, read the content, decide what it is (e.g., a utility bill, a personal
letter, or a promotional flyer), and then put it in the appropriate pile.
In the context of Intelligent Document Processing (IDP), it is as if you had a super-smart
robot assistant to do this for you. This robot doesn’t just look at the envelope or the layout of
the letter (which would be akin to older OCR technologies), it actually ‘reads’ and
‘understands’ the content of each letter. It knows that a letter with “Dear Customer, your
electricity usage this month was…” is a utility bill, and a letter that starts with “Hi, How are
you?” is a personal letter.
So, the robot, like IDP, can sort the letters into the right piles, but it does it much faster and
without any manual effort on your part. Plus, it can handle thousands of letters in the time it
takes you to sort through a handful. That’s the power of IDP in a real-life context!
Different sectors are at various stages of integrating IDP. For instance, lenders who
supported the Paycheck Protection Program (PPP) have extensively used IDP AI to expedite
the review of pandemic loan applications. On the other hand, many mortgage lenders lag in
adopting IDP and still rely heavily on manual document processing.
One of the significant advantages of IDP is its scalability. Whether you are dealing with a
small number of documents or a vast processing operation, IDP can adapt and handle the
task efficiently. It drastically reduces the workforce requirements for managing and
processing documents. However, human involvement is still necessary to some degree.
While both automated and intelligent document processing belongs to the same
technological family, they exhibit unique characteristics that set them apart.
Automated Document
Processing (ADP)
Intelligent Document Processing (IDP)
Scope of
Processing
Primarily focused on
converting physical
documents into digital
format.
Beyond digitization, IDP can understand,
classify and extract information for further
analysis and insight generation.
Error
Handling
Errors or inaccuracies may
need manual intervention for
correction.
With its self-learning ability, IDP can correct
its mistakes over time and improve
accuracy.
Integration
with other
systems
May require additional
software or systems to
manage and make use of the
digitized data.
Often integrated with other enterprise
systems (like ERP, CRM, etc.) to directly
feed and use the extracted data.

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Speed and
Efficiency
Speed and efficiency may
vary depending on the
complexity of the documents.
Typically faster and more efficient as it can
handle large volumes of complex documents
and improve over time.
Cost Initial costs might be lower,
but manual error correction
and additional software
requirements could increase
overall costs.
Although initial costs might be higher due to
the advanced technology, it can lead to
significant savings over time due to higher
efficiency, accuracy, and reduced manual
intervention.
The actual differences may vary based on the specific ADP and IDP solutions being
compared. Each solution might have its own unique features and capabilities beyond the
general differences listed above.
What can IDP do?
Data extraction
At the heart of Intelligent Document Processing (IDP) lies the capability to automate the
extraction of data from complex, unstructured documents – a task that has traditionally been
labor-intensive and required specialized human expertise. IDP systems leverage
sophisticated technologies like natural language processing, optical character recognition
(OCR), and machine learning to understand and extract relevant information from these
documents.
Firstly, OCR technology digitizes documents, converting images and handwriting into
machine-readable text. However, OCR alone isn’t sufficient for extracting meaningful
information, especially from unstructured data like emails, invoices, contracts, etc. This is
where NLP and ML come in.
NLP allows the IDP system to understand the context and semantics of the text, much like a
human would. It can sometimes understand language patterns, interpret meanings, and even
understand the sentiment. Coupled with ML, the system can continuously learn from its
experiences, improving its accuracy over time.
Machine learning algorithms are trained on large datasets to recognize specific data points in
a document, like names, dates, amounts, etc. and extract them accurately. Whether rule-
based or leveraging advanced deep learning, these algorithms possess the ability to
comprehend and extract valuable information from highly complex and varied document
structures.
This advanced data extraction capability streamlines the process and enhances the accuracy
and efficiency of data entry, reducing errors associated with manual data handling.

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Document classification and categorization
A crucial feature of IDP is its ability to classify and categorize documents automatically.
Advanced machine learning algorithms and natural language processing techniques power
this ability.
The first step in the classification process involves using Optical Character Recognition
(OCR) to convert the text present in the documents into a machine-readable format. Once
the text data is available, Machine Learning (ML) models, often supervised models trained
on labeled datasets, are used to classify the documents. These ML models may use a
variety of features to classify documents, such as the presence of certain words or phrases,
the structure of the document, or other identifiable patterns.
In addition, NLP techniques can be employed to understand the context of the document,
which can further enhance the classification process. For instance, semantic analysis, a
subset of NLP, can help understand the meaning of the text and classify it accordingly.
For scenarios where multiple documents are present in a single image or file, advanced IDP
systems use segmentation techniques to separate each document before classifying them.
Computer vision algorithms often guide this process, which can identify boundaries and
structures within the image to segment different documents accurately.
Once documents are appropriately classified and categorized, they can be routed to specific
workflows or processes. This automated sorting and routing significantly reduces the
document processing time and the chances of human error or bottleneck in processing,
making IDP a highly efficient solution for managing large volumes of varied documents.
Data validation
IDP systems significantly enhance data quality and accuracy through the process of data
validation. This process is facilitated by a combination of advanced algorithms and AI
technologies, ensuring the extracted data is reliable and ready for further processing or
analysis.
The data validation process in IDP can be broadly divided into several steps. First, once the
data is extracted from a document, it is initially checked for completeness and consistency.
This involves ensuring that all necessary fields have been captured and the extracted data
adheres to the expected format or pattern.
Next, advanced AI algorithms cross-verify the extracted data against predefined business
rules. These business rules can include data type restrictions, value range constraints, or
specific business logic requirements. For instance, an invoice date shouldn’t be in the future
or an order number should follow a specific pattern. The extracted data is validated against
these rules to ensure its accuracy and relevance.

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In addition to business rules, IDP can leverage machine learning and natural language
processing techniques to compare the extracted data with information from other documents
or sources. For example, it can cross-check the details of an invoice with the corresponding
purchase order to ensure consistency.
Moreover, IDP systems can utilize external databases or data sources for validation,
confirming the accuracy of the extracted data against trusted third-party information.
In the event that the extracted data fails the validation checks, the specific data fields can be
flagged for manual review or correction. This ensures that inaccurate or unreliable data
doesn’t progress further into business processes.
By implementing these techniques, IDP significantly improves data quality, reduces the risk
of errors, and ensures the data’s reliability, ultimately leading to more accurate business
insights and decision-making.
Intelligence and insights
Intelligent Document Processing (IDP) extends beyond mere data extraction and validation.
It plays a pivotal role in converting raw data into actionable intelligence and insights, enabling
businesses to make data-driven decisions.
Once the data is extracted and validated, IDP systems employ various AI technologies such
as machine learning, natural language processing, and text analytics to analyze and interpret
the data. Here is how:
Semantic analysis: Using NLP, IDP can understand the context and semantics of the
extracted data. It can recognize patterns, trends and anomalies in the data, providing a
deeper understanding of the information contained in the documents.
Predictive analysis: Leveraging ML algorithms, IDP can predict future trends or
behaviors based on the analyzed data. For instance, it can forecast customer behavior,
market trends, or potential risks, helping businesses be proactive rather than reactive.
Sentiment analysis: This is particularly useful for customer-facing businesses. IDP
can assess sentiments from customer communications or feedback, helping to improve
customer experience and satisfaction.
Data visualization: IDP can present the analyzed data in intuitive visual formats like
graphs, charts, and dashboards, making it easier for decision-makers to comprehend
complex data and derive insights.
Integration with business intelligence tools: IDP systems can seamlessly integrate
with existing Business Intelligence (BI) tools, feeding them with high-quality, structured
data, enhancing the accuracy and reliability of business reports and analytics.

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With IDP, businesses can transform unstructured data from their documents into strategic
insights. This not only improves operational efficiency but also drives innovation and growth.
However, the specific analytical capabilities can vary among IDP systems, so it’s crucial to
clarify what functionalities a particular system offers before implementation.
How IDP works: The detailed workflow
Pre-Processing Intelligent
Document
Classification
Data
Extraction
Domain
Specific
Validation
Enhanced
Validation
Human-in-
the-Loop
Validation
LeewayHertz
IDP employs a sophisticated workflow that seamlessly combines various technologies to
automate the process of data extraction and analysis from complex, unstructured
documents. This workflow significantly streamlines document management and allows
businesses to access and utilize their data more effectively.
The IDP workflow commences with the capture of information from paper-based documents.
Specialized scanning devices are used to transform these physical documents into digital
formats. These digital documents then serve as the input for the IDP system.
Once the documents are digitized, the IDP system employs computer vision algorithms to
recognize and understand the layout of different document types. These algorithms are
highly versatile and can effectively process scanned images, PDF files, and a plethora of
digital and paper-based file types.
The next stage in the IDP workflow involves natural language processing which is capable of
identifying characters, symbols, letters, and numbers from paragraphs, tables, or
unstructured text within the documents. This identification process, known as Optical
Character Recognition (OCR), is further enhanced by employing techniques such as named
entity recognition, sentiment analysis, and feature-based tagging. The result is a highly
accurate interpretation of the information contained in the documents, with accuracy rates
often exceeding 99%.
Once the information is successfully read, it is then transferred into content management
systems. This process allows the data to be easily accessed, analyzed, and utilized for a
variety of business applications.

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With this introductory understanding of how IDP works, let’s delve deeper and understand
the key steps in the IDP workflow:
Step 1: Preprocessing of document
In IDP, data extraction starts with Optical Character Recognition (OCR). When a document
enters the IDP system, it begins with a step known as document preprocessing. The
effectiveness of OCR heavily relies on its ability to distinguish characters or words from the
document’s background accurately. There are a few key techniques used in this initial phase:
Binarization: Binarization converts a colored image into black and white pixels with
black (pixel value = 0) and white (pixel value = 256). The goal here is to clearly
distinguish between the text characters (black pixels) and the background (white
pixels).
Deskewing: The resulting image may be slightly tilted horizontally during scanning. This
misalignment isn’t ideal for OCR, so techniques like the Projection Profile method,
Hough Transformation method, and the Topline method are employed for correcting
this skew.
Noise removal: This step eliminates any small, unwanted dots or patches. This cleanup
is essential to prevent OCR from mistaking these elements for actual characters.
Step 2 – Document classification
The classification of documents within the IDP workflow unfolds in three stages:
Format identification: The system first determines the file format of the document. It
discerns whether the document is a PDF, JPG, PNG, TIFF, or any other supported file
format.
Structure recognition: Next, the IDP solution distinguishes between structured, semi-
structured, and unstructured documents. Structured documents follow a consistent
template and layout. On the other hand, semi-structured documents have some degree
of structure but can contain similar information at varying locations within the
document. For instance, an invoice, which is a semi-structured document, might have
the vendor’s address positioned differently across various invoices. To make sense of
such data, the IDP solution requires a contextual understanding of the document and
its content. Unstructured documents have minimal structure, yet they often contain
critical data that needs to be extracted. For example, contracts are usually
unstructured, with certain values such as dates or email addresses not being clearly
identified.

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Document type determination: The final step in document classification involves
identifying the type of document, i.e., identifying whether it’s an invoice, bank
statement, tax document, shipping label, or some other form. The IDP solution’s
success in accurately identifying and routing a document type for data extraction
depends largely on the data it has been trained on.
Step 3 – Data extraction
The extraction of data within the IDP workflow typically consists of two main components:
i) Extraction of key-value pairs: This involves pulling out the values that correspond to distinct
key identifiers within a document.
ii) Table extraction: This process involves extracting line items organized in a tabular format.
Several methods are employed to accomplish these tasks:
OCR (Optical Character Recognition): OCR constitutes the initial phase of data extraction.
While this step is crucial, certain errors can occur during OCR, such as:
Word detection error occurs when the system fails to identify a text block in the image,
often due to poor image quality.
Word segmentation error: This happens when a word is interpreted incorrectly due to
misidentification of interword spaces, varying text alignments, and spacing issues.
Character segmentation error: This refers to the system’s inability to detect single
characters within a segmented word, a common issue with cursive or connected
alphabets.
Character recognition error occurs when the system fails to correctly identify a
character within a bounded character image. Techniques like dictionary look-up, k-mer,
and n-gram language models can help rectify these errors.
Rule-based extraction: Rule-based models are effective for structured and semi-structured
documents. They can identify key-value pairs or line items by referencing positions within a
document. Approaches like Named-Entity Recognition and the n-gram model are useful for
identifying values associated with key identifiers. For instance, regardless of the placement
of the invoice number in an invoice, the model searches for a set of strings adjacent to
“Invoice Number” or “Invoice No.”
Learning-based approach: Deep learning and machine learning hybrid data extraction
techniques require supervised or unsupervised learning for training their models. Their
accuracy rate and confidence score measure the efficiency of these models. As the volume
of processed documents increases and the models receive more training and feedback, their
accuracy improves. For instance, an ML-based model could be used in conjunction with a

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template-based OCR system to improve accuracy. Simple OCR correction methods
combined with context-based natural language processing can enhance the quality and
precision of extracted data.
Step 4 – Data validation
Data validation is a crucial stage in the IDP workflow, focusing on verifying and assuring data
accuracy. This stage leverages advanced algorithms and pre-established rules to identify
any discrepancies or anomalies in the extracted data.
Several techniques can be used in this process:
Rule-based validation: This approach applies specific rules to the data. For instance,
an invoice’s ‘total payable amount’ should match the sum of the ‘subtotal’ and ‘tax
payable’. If there’s a mismatch, the system flags the document for review.
Cross-document verification: This technique involves comparing the extracted data
against other relevant documents or data sources. For instance, the system could
cross-check the extracted invoice amount against a corresponding purchase order or
contract agreement.
Machine learning validation: Machine learning models trained on historical data can
predict expected data values and flag anomalies. These models can be especially
useful when dealing with large data volumes, providing an additional layer of validation
to the process.
External database validation: For some types of data, validating against an external
database or API may be possible. For instance, a system could validate address data
against a postal address database or a company name against a business registry.
By combining these approaches, IDP systems can ensure high levels of data accuracy,
reducing the risk of errors propagating downstream in business processes. However, it’s
essential to note that data validation is an ongoing process, requiring regular review and
updates to rules and models as business requirements and data structures evolve.
Step 5 – Enhanced validation
Enhanced validation in the IDP process can be significantly bolstered with the use of Robotic
Process Automation (RPA). RPA, with its ability to automate repetitive, rule-based tasks, is
particularly suited for streamlining data validation.
Data is initially extracted from various documents using IDP technologies like OCR and ML
in an IDP workflow. This extracted data can contain a variety of details such as names,
dates, account numbers, transaction specifics, among others. Following the extraction, the
data undergoes an initial validation where basic validation rules are applied. This could
include checking if all necessary fields have been populated, confirming that numerical fields
contain actual numbers, or validating that dates conform to the expected format.

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At this juncture, RPA can be employed for a deeper, enhanced level of validation. For
instance, RPA can cross-verify the extracted data with information from other systems or
databases. If a document contains a customer’s name and account number, an RPA bot
could access the customer database to validate that the name and account number
correspond correctly. While this task would be labor-intensive and time-consuming for a
human to perform manually, an RPA bot can quickly and accurately carry it out.
In the event of a discrepancy identified during the RPA validation process, the bot can flag
the document for review. This allows a human operator to inspect the document and rectify
any errors manually. This integration of human judgment ensures that the validation process
remains both efficient and precise.
Moreover, the combined use of RPA and ML can facilitate an environment of continuous
learning. If errors are identified and corrected during the validation process, this information
can be fed back into the machine learning model, thereby enhancing its accuracy over time.
Consequently, by integrating RPA into the IDP workflow, organizations can significantly
elevate their data validation processes’ accuracy and efficiency, leading to notable time and
cost savings and improved data quality.
Step 6 – Human review
While IDP aims to achieve complete automation, it’s important to acknowledge that no data
extraction model can guarantee 100% accuracy. Thus, the IDP workflow incorporates an
essential human element – the human-in-the-loop. This involves manual review and
validation of any documents that have been flagged for potential inaccuracies during the
extraction process.
This human intervention serves two critical purposes. First, it ensures that the final data
output is as accurate as possible, reinforcing the reliability of the IDP system. Second, it
contributes to the supervised learning of the model, gradually enhancing its accuracy. This
continuous process cycle of processing, reviewing, and learning helps the model evolve over
time, increasing its performance as more documents are processed.
The processed data is ready to be integrated into the user’s workflow upon successful
extraction and validation. The IDP system has the flexibility to push this data to a database
or export it in various formats to suit the user’s needs. Be it JSON, XML, PDF, or any other
format, IDP workflows offer the versatility to convert documents into a format that best fits the
user’s system or requirement.
The key components of intelligent document processing
Optical Character Recognition (OCR)

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Optical Character Recognition, commonly referred to as OCR, is a fundamental technology
used in IDP. It’s the technology that enables computers to understand and convert different
types of documents, such as scanned paper documents, PDF files, or images captured by a
digital camera, into editable and searchable data.
At its core, OCR technology analyzes the shapes and patterns of an image’s dark and light
areas to identify each character. Advanced OCR systems can recognize multiple fonts and
languages, making them highly versatile.
In the context of IDP, OCR is the first step in the process of extracting valuable data from
unstructured documents. It ‘reads’ the text from the document and converts it into a format
that can be processed and understood by the rest of the IDP system. Without OCR, the
system would not be able to ‘see’ the text in the document, making further processing
impossible.
However, OCR is not infallible and is subject to errors due to poor image quality, unusual
fonts, or complex layouts. To overcome these challenges, IDP systems employ advanced
techniques such as image pre-processing to improve the quality of the input images, or
machine learning to improve the OCR’s ability to recognize and interpret text correctly.
Another layer of complexity in OCR within IDP comes from the need to understand and
process not just individual characters but also how those characters form words, sentences,
and ultimately, meaningful content. This is where NLP comes in. NLP is a field of AI that
focuses on the interaction between computers and humans through natural language. In
conjunction with OCR, NLP enables IDP systems to ‘understand’ the content in the
documents, making it possible to extract not just raw data, but valuable, actionable
information.
Machine learning and artificial intelligence
Machine learning and artificial intelligence serve as the critical engines powering intelligent
document processing. They help transform unstructured data into structured information and
extract meaningful insights from it.
Machine learning: In the context of IDP, ML algorithms learn from training data, which
includes a variety of documents and the correct output for each document. Over time, these
algorithms ‘learn’ to recognize patterns and structures in the documents and improve their
ability to extract the correct information.
Two main types of ML are used in IDP: supervised learning and unsupervised learning. In
supervised learning, the algorithm is trained on a labeled dataset, where each document is
paired with the correct output. On the other hand, unsupervised learning does not require
labeled data; instead, the algorithm identifies patterns and structures in the data on its own.

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ML plays a significant role in several stages of the IDP workflow, including document
classification, data extraction and data validation. For example, ML algorithms can learn to
classify different types of documents based on their content and structure, extract relevant
information from these documents and validate the extracted data based on predefined
rules.
Artificial intelligence: In the context of IDP, AI is the overarching technology that brings
together OCR, ML, and other technologies to create systems capable of processing
documents intelligently.
A key aspect of AI in IDP is NLP, which allows the system to understand, interpret, and
generate human language. NLP enables IDP solutions to handle more complex tasks, such
as understanding the context of information in a document, recognizing entities, and even
understanding sentiments. This is particularly important when dealing with unstructured
documents, where information is not neatly organized in tables or forms.
AI also enables IDP systems to improve over time. As more documents are processed, the
system learns from any mistakes or corrections, becoming more accurate and efficient.
Natural language processing
Natural language processing or NLP plays a pivotal role in IDP that combines computational
linguistics with machine learning and deep learning models to comprehend the intricacies of
human language, making it a key component in IDP. Here’s how:
Text extraction and understanding: NLP aids in extracting and understanding the
text from various types of documents. It can recognize and interpret various text
formats, including paragraphs, bullet points, tables, and even handwritten notes,
making it particularly useful in dealing with unstructured data.
Contextual understanding: One of the biggest challenges in document processing is
understanding the context of information. For instance, the same word could have
different meanings in different contexts. NLP algorithms can interpret the context based
on surrounding text, helping to identify and extract relevant information accurately.
Named Entity Recognition (NER): NER is an NLP task that identifies and classifies
named entities in text into predefined categories such as names of persons,
organizations, locations, expressions of times, quantities, monetary values,
percentages, etc. In IDP, NER helps to identify specific data points within the document
text, such as the name of a person, a company name, or an invoice number.
Information Extraction (IE): NLP is also critical in information extraction, where the
goal is to convert unstructured text into structured data. This involves tasks like
extracting relationships between named entities, identifying the sentiment expressed in
the text, or recognizing specific events or facts.

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Text classification and categorization: NLP enables IDP systems to classify and
categorize documents based on their content automatically. This is done through text
classification techniques, which can sort documents into predefined categories.
Error detection and correction: NLP also plays a crucial role in error detection and
correction in IDP. It can identify anomalies or errors in the extracted data, such as
misspelled words or incorrect grammar, and correct them based on the context.
Continuous learning: One of the significant advantages of NLP is its ability to learn
and improve over time. As more documents are processed, the NLP algorithms can
learn from any corrections or feedback, becoming more accurate and efficient.
The role of AI and ML in intelligent document processing
Artificial intelligence and machine learning play a pivotal role in intelligent document
processing. They enable the automation of processes, extraction of insights from
unstructured data, and continuous learning and improvement. Here’s a detailed look at the
various ways AI and ML contribute to IDP:
Automated document classification: AI algorithms can automatically classify
documents based on their structure and content. Machine learning models can be
trained to recognize different types of documents, such as invoices, contracts, or
receipts, and categorize them accordingly. This automation accelerates document
processing and reduces the need for manual intervention.
Data extraction: AI and ML are at the heart of the data extraction process. ML models
can be trained to identify and extract specific information from various documents. For
instance, an ML model can learn to identify an invoice number or the total amount in an
invoice. Similarly, AI technologies like OCR can convert different types of handwritten,
typed, or printed text into machine-encoded text.
Natural language processing: As a subset of AI, NLP plays a crucial role in IDP. NLP
enables the system to understand and interpret human language, extracting and
analyzing information from unstructured data such as emails, reports, and articles.
Data validation: AI and ML models can validate the extracted data by comparing it
with predefined business rules or other data sources. They can flag potential
inaccuracies or inconsistencies for review, ensuring the quality and accuracy of the
extracted data.
Continuous learning and improvement: One of the most significant benefits of AI
and ML in IDP is their ability to learn and improve over time. As more documents are
processed, the models can learn from any corrections or feedback and adapt their
algorithms to improve accuracy and efficiency. This continuous learning capability is
crucial for handling the complexity and variability of unstructured data.

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Predictive analysis: Machine learning algorithms can analyze historical data to predict
future trends or behaviors. In the context of IDP, this could involve predicting the
likelihood of errors in a particular type of document or identifying potential bottlenecks
in the document processing workflow.
Insight generation: AI and ML can generate insights from the extracted data beyond
just processing documents. This could involve identifying data patterns, trends, or
anomalies, which can inform strategic decision-making.
Use cases of IDP
Intelligent document processing offers various use cases across various industries, helping
streamline operations, improve accuracy, and drive efficiencies. Here is a look at how IDP
can be applied in different sectors:
Lending: In the finance industry, IDP solutions can automate loan application
processing, thereby significantly reducing manual data entry tasks and speeding up
turnaround times. For instance, IDP can validate and verify customer data, credit
reports, personal identification documents and income verification documents in
mortgage loans. This ensures a more efficient and accurate credit risk analysis and
quicker loan approvals.
Insurance: The insurance industry can leverage IDP to manage large volumes of
customer data and conduct credit profile analyses. For instance, an insurance
company could use IDP to process and analyze application forms, health records, or
claim documents. By automating these processes, insurers can better assess risk, set
premium rates, and offer personalized benefits to their customers.
Logistics: The logistics industry often deals with a vast amount of data that needs to
be validated, verified, and cross-checked. IDP can automate the processing of
documents such as invoices, labels, and agreements, thereby eliminating the need for
manual input and reducing the likelihood of errors. For instance, a shipping company
could use IDP to automate the processing of shipping labels or invoices, leading to
faster and more efficient operations.
Commercial real estate: In the commercial real estate industry, IDP can be used to
process documents like rent rolls, lease agreements, offering memorandums, and
operating statements. For example, a property owner could use IDP to analyze lease
agreements and determine the potential return on investment for renting, leasing, or
buying new properties. This allows for more informed decision-making and can lead to
more lucrative investments.

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Accounts payable: IDP can transform accounts payable operations by automating the
processing of invoices and matching them against purchase orders in real time.
Regardless of the layout or structure of the invoices, an IDP solution can accurately
extract the relevant data and match it against the corresponding purchase orders. This
automation reduces manual work and ensures accuracy and efficiency in the accounts
payable process.
The technology stack of IDP
The technology stack of intelligent document processing typically includes a variety of tools
and technologies, each with their unique roles in the IDP workflow. Here is a tabular
representation of some of the key components of an IDP technology stack:
Technology
Category
Specific
Technology/Tool
Role
Optical
Character
Recognition
(OCR)
Tesseract, Abbyy,
Google Cloud Vision
OCR
Converts different types of documents, including
paper, PDF files, and photos into data that
machines can process.
Machine
Learning
(ML)
TensorFlow, PyTorch,
Scikit-learn
Trains models to improve accuracy in data
extraction and validation over time. Used in
conjunction with OCR for extracting data from
complex documents.
Natural
Language
Processing
(NLP)
NLTK, SpaCy,
Stanford NLP
Helps understand, interpret, and manipulate
human language, allowing for the extraction of
data from unstructured text.
Artificial
Intelligence
(AI)
OpenAI, IBM Watson,
Google AI
Enables the system to learn and adapt from
experience, improving its performance as it
processes more documents.
Robotic
Process
Automation
(RPA)
UiPath, Blue Prism,
Automation
Anywhere
Automates repetitive tasks such as data entry,
cross-verifications, and validations, thereby
enhancing efficiency.
Computer
Vision
OpenCV, TensorFlow Helps in recognizing different document layouts,
even within a single image, and identifying and
categorizing documents for further processing.
Cloud
Platforms
AWS, Google Cloud,
Microsoft Azure
Provides a scalable infrastructure to host and run
the IDP solutions, offering benefits like ease of
access, security, and scalability.

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Technology
Category
Specific
Technology/Tool
Role
APIs/SDKs RESTful APIs,
GraphQL
Facilitates integration of IDP with other systems,
enabling end-to-end document processing and
data exchange across various applications.
Databases SQL (like
PostgreSQL,
MySQL), NoSQL (like
MongoDB,
Cassandra)
Used for storing extracted and validated data,
acting as a single source of truth for downstream
applications and processes.
This table only scratches the surface of a comprehensive IDP tech stack and the exact
technologies involved can vary based on specific use-cases and vendor solutions.
Benefits of intelligent document processing
Intelligent document processing offers an array of benefits that significantly enhance
operational efficiency and effectiveness across various business sectors. Here is a detailed
exploration of these advantages:
Enhanced efficiency: The primary benefit of IDP is the significant boost in operational
efficiency it brings. By eliminating manual data entry, IDP drastically reduces
processing times, particularly beneficial for organizations handling large volumes of
unstructured data. The automation of mundane tasks allows employees to focus on
more strategic aspects of the business, thereby enhancing productivity.
Improved accuracy: Research shows that manual data entry into even simple
spreadsheets carries an error rate between 18% to 40%, a figure that surges to 100%
with complex spreadsheets. On the other hand, IDP systems boast an accuracy rate of
at least 95%, mitigating the substantial risks associated with manual document
processing. This heightened accuracy leads to more reliable data and less time spent
on error corrections.
Cost efficiency: IDP’s automation capabilities significantly diminish labor costs by
handling repetitive and time-consuming tasks. Moreover, it curtails expenses linked to
errors and inaccuracies, providing a clear avenue for cost savings.
Informed decision making: IDP’s ability to extract valuable insights from unstructured
data simplifies and enhances decision-making processes. This is particularly
advantageous for industries that rely on data-driven decisions, such as finance,
healthcare, and government sectors. With accurate, readily available data, businesses
can make informed decisions swiftly and confidently.

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Seamless integration: IDP systems can easily integrate with other systems like
databases or business intelligence tools for further analysis and reporting. This
integration ensures that businesses can readily access and utilize the extracted data,
bypassing the need for manual data input into other systems.
Boosted employee productivity: By eliminating manual corrections, IDP improves the
employee experience, leading to quicker approvals and reduced processing times.
Furthermore, it allows employees to concentrate on more intellectually challenging
tasks rather than manual corrections, thereby increasing operational productivity and
job satisfaction.
Implementing intelligent document processing
Considerations when choosing an IDP solution
When selecting an IDP solution, a number of considerations should be evaluated to ensure it
aligns with your organization’s specific needs.
Start by understanding your data processing needs. This entails identifying the format in
which your data is received or stored (email, scanned document, physical paper, etc.),
determining whether your data is structured or unstructured and assessing the volume and
frequency of data you receive and the proportion that needs to be automated.
After pinpointing your initial needs, ascertain which datasets would be optimal for IDP.
Documents that consume a significant amount of time for manual processing are prime
candidates.
Once these datasets are identified, the focus shifts to choosing the IDP software. Key factors
to consider include the expected accuracy level versus manual error rates and the potential
for improvement, whether the IDP technology is template-based or equipped to manage
complex data formats that lack a defined structure, and the software’s ability to read and
comprehend all types of data and documents you currently handle.
Further considerations include the software’s compatibility with your chosen business tools,
its capacity to handle your anticipated data volume, scalability, setup time, and the level of
support available. Lastly, it’s crucial to compare competing quotes to gain a clearer
perspective on pricing.
Steps to implement IDP
Implementing intelligent document processing in your organization can transform the way
you manage data. Here is a step-by-step guide to implementing an IDP solution:
Step 1: Define your requirements

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Identify the problems you are aiming to solve with an IDP solution. This could range from
reducing manual data entry to improving data accuracy. Clearly defining your requirements
will help you choose the right IDP solution. The possible categories in defining requirements
for implementing an IDP solution can include:
Business requirements: This would define the specific business problems you are
aiming to solve. This could be reducing data entry errors, accelerating data processing,
achieving regulatory compliance, or reducing labor costs.
Data requirements: This involves understanding the nature and format of the data you
handle. You need to define whether your data is structured or unstructured, the types of
documents you work with (invoices, forms, emails, etc.), the languages these
documents are in, and the volume and velocity of data your organization handles.
Technical requirements: These requirements pertain to the IDP solution’s
compatibility with your existing IT infrastructure. It includes things like integration
capabilities with your existing systems, hardware and software requirements,
scalability, and security needs.
Operational requirements: This involves defining how the IDP solution will fit into your
existing workflows. This includes user roles and access levels, turnaround times for
document processing, and the level of human intervention needed in the process.
Financial requirements: This would define your budget for implementing the IDP
solution, taking into account both the upfront costs of the software and the ongoing
costs for maintenance, updates, and potential scaling needs.
Vendor requirements: This category involves defining what you expect from the IDP
solution provider. This could include requirements related to customer support, training
for your staff, assistance with initial setup and integration and their track record and
reliability.
Defining these requirements thoroughly will help you select an IDP solution that aligns with
your organization’s needs and goals, thereby maximizing the value you gain from the
technology.
Step 2: Understand your data
Evaluate the type of data you handle. Is it structured or unstructured? What’s the format of
the data (email, PDF, scans, etc.)? Understanding the nature of your data will guide you in
selecting an IDP solution that can effectively process your data. When choosing an IDP
solution, understanding the different techniques used by various IDP solutions to process
and understand data is crucial. These techniques can significantly impact the performance
and suitability of the solution for your specific use case. Some key techniques used for this
are optical character recognition, Intelligent Character Recognition (ICR), machine learning,
natural language processing, computer vision, robotic process automation and data
validation. Understanding these techniques can help you evaluate how well an IDP solution

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can meet your specific document processing needs. It’s also a good idea to ask potential
vendors for demonstrations or case studies showing how their solution has successfully
been used in similar scenarios to yours.
Step 3: Choose the right IDP solution
When it comes to implementing intelligent document processing, there are various types of
solutions available in the market, each with its unique strengths and capabilities. Here are
some of the key types of IDP solutions:
OCR-based solutions: These solutions primarily focus on converting printed text into
machine-encoded text. They are excellent for processing structured documents, such
as forms and invoices, where the data fields are located in the same place every time.
Machine learning-based solutions: These solutions leverage machine learning
algorithms to learn from the data and improve over time. They are particularly good at
handling semi-structured and unstructured documents, as they can learn to identify
patterns and relationships within the data.
AI-powered solutions: AI-powered IDP solutions go a step further by employing
advanced technologies like natural language processing and deep learning to
understand the context of the data. They can handle complex tasks like sentiment
analysis, entity extraction, and more.
RPA-integrated solutions: These solutions combine the power of IDP with robotic
process automation. They are capable of not only extracting and processing the data
but also automating the subsequent steps in the workflow, such as data entry into a
database or ERP system.
Hybrid solutions: Hybrid IDP solutions combine several of the above technologies to
offer a comprehensive solution. They can handle a wide variety of document types and
complexities, making them a versatile choice for businesses with diverse document
processing needs.
Cloud-based solutions: These IDP solutions are hosted on the cloud and offer
scalability, easy access, and often a pay-as-you-go pricing model. They are a good
option for businesses that want to avoid the upfront costs and maintenance associated
with on-premise solutions.
On-premise solutions: For businesses that prefer to keep their data in-house due to
security or compliance reasons, on-premise IDP solutions would be a better choice.
They are installed and run on the company’s own servers and infrastructure.
Choosing the right IDP solution depends on your business needs, the type and complexity of
the documents you process, your IT infrastructure, and your budget. It is always a good idea
to request a demo or a trial before making a final decision. Compare different IDP solutions
considering their capabilities, accuracy, scalability, ease of integration with your existing
systems, and cost. The solution should be able to handle your data volume and complexity,
and align with your organization’s future growth.

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Step 4: Set up the IDP system
Implementing an IDP solution requires careful configuration and setup to ensure that the
system can correctly recognize and process your specific documents and data fields. Here is
how this process typically unfolds:
Understanding document types: First, the IDP system needs to understand the
different types of documents it will be dealing with. This could range from invoices and
forms to letters and contracts. Each document type has its unique layout, structure, and
data fields.
Defining data fields: For each document type, you will need to define the specific data
fields the system should extract. This could be anything from names and addresses on
forms to item descriptions and prices on invoices.
Training the IDP system: Next, the IDP system is trained using a set of sample
documents. The system learns to recognize the different document types and the
locations of the data fields within them. If the system uses machine learning, this
training process will involve feeding it with numerous examples until it can accurately
identify and extract the required data.
Configuring the IDP software: The software then needs to be configured to process
the documents according to your specific requirements. This could involve setting up
rules for data validation, defining workflows for how the extracted data should be
processed, and determining what actions should be taken when exceptions occur.
Integration with existing systems: The IDP system also needs to be integrated with
your existing IT infrastructure. This could involve setting up connections to your
databases, ERP systems, or other business applications where the extracted data will
be stored or further processed.
Testing and optimization: Finally, the setup process involves testing the IDP system
with real documents to ensure that it can accurately extract and process the required
data. Any issues or inaccuracies discovered during this testing phase would need to be
addressed, and the system fine-tuned for optimal performance.
Throughout this setup process, your IDP vendor should provide support and guidance. They
will likely have a team of experts who can assist with configuring the system, training the AI
models, integrating with your existing systems, and troubleshooting any issues that arise.
Step 5: Train the system
Training an intelligent document processing system is a crucial step in its implementation.
The goal is to enable the system to accurately identify, extract, and process data from
diverse document types. Here is a step-by-step explanation of the process:
Sample document collection: The first step is gathering a diverse set of sample
documents that the system will likely encounter. These documents should represent
various types and formats the IDP system needs to handle.

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Data annotation: Once the sample documents are collected, they need to be
annotated. This process involves manually marking up the documents to highlight the
information that the IDP system needs to extract, such as names, addresses, invoice
numbers, etc. This annotated data serves as the “ground truth” that the system will
learn from.
Model training: once the annotated documents are ready, they are fed into the IDP
system. The system’s machine learning algorithms use this data to learn patterns and
structures of the documents, and how to correctly identify and extract the required data
fields. This phase is iterative and may require adjustments to the algorithms or
additional training data to improve accuracy.
Validation and testing: After the initial training, the system needs to be tested to
assess its performance. This involves feeding it with new documents (not used in the
training phase) and comparing the system’s output with the actual data. This helps in
understanding the model’s accuracy and identifying any areas that need improvement.
Model tuning: Based on the results of the validation and testing phase, the model may
need to be fine-tuned. This could involve adjusting the model’s parameters, providing
additional training data, or even changing the model structure in more complex cases.
Active learning: As the system is used in real-world conditions, it continues to learn
and improve over time. Any errors that the system makes can be corrected and fed
back into the system for further learning. This process, known as active learning, allows
the IDP system to continually adapt to changing document formats and improve its
performance over time.
Remember, the goal of training an IDP system is to achieve a high level of accuracy in data
extraction, minimize manual intervention, and ensure the system can handle a variety of
document types and structures.
Step 6: Test and refine
In the implementation of an intelligent document processing system, testing and refinement
is a crucial phase designed to ensure the accuracy of data extraction and the overall
performance of the system. It’s an iterative process involving several steps:
Initial testing: Once the IDP system is set up and trained, it’s tested using real-world
documents that haven’t been used during the training phase. This allows for an
unbiased evaluation of how well the system performs when confronted with new,
unprocessed data.
Evaluation: The system’s output is compared with the actual data from these
documents. Specifically, it is the accuracy of the extracted data that is evaluated. This
involves checking whether the system has correctly identified and extracted the
necessary data fields. For instance, if the system is designed to extract invoice
numbers, dates, and amounts from invoice documents, you would check whether these
details have been correctly extracted from the test documents.

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Error identification: Any discrepancies between the actual data and the system’s
output are identified. This could involve errors in data extraction, misinterpretation of
document structures, or failure to recognize certain data fields. The source of these
errors is then investigated.
Refinement: Based on the results of the evaluation and error identification,
adjustments are made to the system. This could involve refining the machine learning
algorithms, providing additional training data, or making changes to how the system
interprets different document types.
Iteration: The testing and refinement process is repeated until the system’s
performance reaches an acceptable level. This involves running the refined system on
new test documents, evaluating its performance, identifying any errors, and making
further refinements.
Continuous improvement: Even after the system is deployed, it’s essential to
maintain a feedback loop for continuous improvement. This involves regularly testing
the system with new documents, assessing its performance, and making ongoing
refinements.
It is worth noting that the testing and refinement phase could require several iterations before
the system’s performance is optimized. This is because each adjustment made to the system
during the refinement phase could potentially impact how it interprets and processes
documents.
Step 7: Integrate with existing systems
In the IDP implementation process, integrating the IDP solution with your existing systems,
such as Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP)
software, is a pivotal step. This integration allows the IDP system to automatically feed the
extracted data into these systems, streamlining your workflows and eliminating the need for
manual data entry. Here is how this integration typically unfolds:
Understanding the existing infrastructure: Before integration, a thorough
understanding of your existing system infrastructure is crucial. This includes knowing
the software interfaces, data formats, and how data flows between different systems.
API integration: Most modern IDP solutions offer Application Programming Interfaces
(APIs) that enable seamless communication between different software applications.
Using these APIs, the IDP system can be connected to your CRM or ERP system. The
IDP system sends data using a format and protocol that the CRM or ERP system can
understand and process.
Data mapping: This involves defining how data extracted by the IDP system
corresponds to fields in the CRM or ERP system. For example, if the IDP system
extracts invoice numbers and amounts, these need to be mapped to the corresponding
fields in your financial system.

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Testing the integration: Once the initial integration is done, it is important to test the
setup to ensure the data is correctly transferred from the IDP system to the CRM or
ERP system. This includes checking that all data fields are correctly populated and that
the data is accurately represented.
Refining the integration: Based on the results of the testing phase, the integration
might need to be refined. This could involve adjusting the data mapping, changing how
data is formatted before it is sent, or making other changes to the integration setup.
Monitoring and maintenance: After the IDP system is fully integrated, it is vital to
continually monitor the data transfer process and maintain the integration. This helps to
ensure that any issues are quickly identified and addressed, and that the integration
continues to work effectively as systems are updated or changed.
By integrating your IDP solution with your existing systems, you can considerably enhance
the efficiency of your business processes and reduce the time and resources spent on
manual data entry tasks.
Step 8: Roll out and monitor
Once you are confident in the system’s performance, roll it out for full use. Regularly monitor
the system’s accuracy and efficiency, and continually retrain it with new data to improve its
performance over time.
Implementing an IDP solution is not a one-time task but a continuous process of
improvement. As your business evolves, so too will your data processing needs. Stay flexible
and keep your IDP system updated to keep pace with your growth.
Future trends in intelligent document processing
As we cast our gaze towards the future of IDP, several trends begin to take shape. You can
think of IDP and RPA as a dynamic duo, like a chef and a waiter in a restaurant. IDP acts like
the chef who prepares and organizes the food, while RPA is the waiter who serves it to the
customers. In a business setting, IDP prepares and organizes the data from documents, and
then RPA comes in to serve or input this data into the various computer systems in a
business. As we move forward, we expect these two to work even more closely together,
making things run more smoothly and efficiently.
The algorithms that underpin IDP, which are primarily rooted in AI and machine learning, are
predicted to undergo substantial evolution. We can anticipate improvements in the use of
Convolutional Neural Networks (CNNs) for image-based document processing, Recurrent
Neural Networks (RNNs), and Long Short-term Memory (LSTM) models for sequential data
processing, and even Transformer models, like BERT or GPT-3, for enhanced natural
language understanding. These advancements will lead to heightened accuracy and an
increased ability to manage more complex and diverse document types.

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There will also likely be a greater emphasis on real-time processing to meet the growing
demand for instant insights from businesses. As IDP technology continues to mature, it is
expected to branch out into new areas such as customer service, extracting critical
information from client communications to facilitate prompt and accurate responses.
With IDP systems often dealing with sensitive data, the future will undoubtedly see a
heightened focus on data privacy and security. Compliance with data protection regulations
and the implementation of sophisticated security measures will become essential.
The future also hints at a move towards cloud-based IDP solutions, which offer scalability,
cost-efficiency, and ease of implementation. Additionally, with the rise of edge computing, we
may see IDP systems deployed closer to the points of data generation, thereby reducing
latency and enhancing real-time processing capabilities.
Lastly, businesses will increasingly seek personalized IDP solutions, tailored to their unique
needs, industry-specific documents, and workflows. These trends paint a future where IDP
becomes an indispensable part of business operations, driving efficiency and extracting
valuable insights from unstructured data.
Endnote
As we look towards the future, IDP is set to undergo further evolution, harnessing the power
of more sophisticated artificial intelligence and machine learning algorithms. These
advancements will allow IDP to tackle an even wider array of complex documents and data
structures, offering greater flexibility and capabilities to businesses.
In today’s data-driven world, where data privacy is of paramount importance, the emergence
of private versions of IDP is a development of immense significance. Such privacy-focused
advancements are set to broaden IDP’s potential even further, offering businesses the
opportunity to protect their sensitive data while still reaping the benefits of automation and
advanced data processing.
Therefore, IDP is not just a testament to the transformative power of AI and ML, but it is also
a beacon, lighting the way towards a future where data processing is not just faster, but
smarter and more efficient; a future where businesses can harness the full potential of their
data, for improved decision-making and operational efficiency, ultimately, driving growth and
success. The future of IDP is bright, and its possibilities are virtually limitless.
Ready to transform your business with intelligent document processing? Leverage
LeewayHertz’s knowledge and expertise working with data, and drive success to your data-
driven business!