What is Intelligent Document Processing (IDP)

ABCAdda | Updated Aug 17, 2022

Intelligent document processing automates data collection from various documents and sources and organizes them for further processing. This technology enables organizations to seamlessly integrate with core processes, eliminate manual labor, overcome challenges they face in reading complex document layouts, and meet regulatory and compliance requirements.

Accurate data is at the core of any organization, and IDP helps companies manage the complexities of processing large amounts of documents by helping them automate manual data entry processes.

So what exactly are intelligent document processing India and its use cases across various industries – that’s what we’ll discover in this blog.

Before seeing Intelligent document processing, let us know what data processing is.

What is document processing?

Document processing is converting physical documents and their related forms into digital forms by data extraction, thereby organizing them in a suitable structured format.

Documents have different formats and file types and contain value-specific information. With the old approach of manual document processing, you are manually involved in document processing, which is error-prone, time-consuming and expensive.

However, you need the information to use in documents to scale up downstream applications in a short time, which is not possible with complex manual document processing.

For this reason, automated document processing solutions are preferred over simple manual document processing. Automating document processing according to advanced technology can benefit you by integrating core processes, eliminating manual labor, meeting compliance requirements, and solving problems when processing complex documents.

How does document processing work?

Data processing can be done using computer vision algorithms, neural networks, or manual processing. Usually, the process of digitizing analog to digital data follows the following steps:

How does document processing work? Categorize and extract layouts and structures, Extract document information, Document error detection and correction and Document and data storage

  • Categorize and extract layouts and structures: Document processing solutions driven by rules. The programmer creates these predefined extraction rules before work can begin. This includes specifying the category and format of the document. Once this is defined, the team can derive a layout and structure.
  • Extract document information: There are several methods teams can use to automate text transcription. Optical character recognition (OCR) scans documents for the typed text from manual documents and converts them into data. Smart Character Recognition, a type of handwriting recognition (HTR), can recognize the standard text and various handwriting fonts and styles.
  • Document error detection and correction: OCR technology can be error-prone, meaning the extracted data may need to be checked manually. If the document format cannot be processed or errors are found, it can be flagged for human review and corrected by manual input.
  • Document and Data Storage: The final document is saved in a format that allows integration with the current application.

When you use intelligent Document Processing, traditional document processing is enhanced by:

  • Faster data processing: Advanced automation is a faster and more accurate way to extract relevant information from analog and unstructured data. It streamlines workflows by eliminating manual processes and reducing errors.
  • Unstructured Document Processing: Unlike traditional document processing, IDP can transform structured, unstructured, and semi-structured information and apply data to business applications and workflows.
  • Improve data accuracy: Machine learning improves document classification, information extraction, and data validation to improve processing quality and reliability. Using supervised low code learning in workflows aims to improve accuracy over time without having to recode extraction rules.
  • Enhanced security: IDP stores documents and personal information in a secure (digital) place. This is especially important in healthcare and finance industries with strict security rules and compliance policies.
  • Cost Reduction: The manual aspect of traditional document processing is time consuming and frees professionals from other work. Automation shortens processing time, reducing operating costs and better-using staff.

What is intelligent process automation?

Intelligent process automation (IPA), also known as hyper-automation, intelligent automation, or digital process automation, is a process that combines processes with robotic process automation (RPA).

Mining, OCR/ICR, analytics, and artificial intelligence (AI) to create business process automation that thinks independently, learns and adapts.

What is intelligent document automation?

With intelligent document automation, you can work efficiently with documents that are faxed or emailed to you. Convert them to digital files; select pages to include or exclude; rotate them as needed; and get their data in Salesforce.

Intelligent document automation works with PDF, JPG and PNG image files. However, PDF files with form field data are not supported.

What is intelligent document processing?

Intelligent document processing (IDP) refers to business processes that use deep learning tools to process documents. Using RPA bots, AI, and computer vision, IDP extracts unstructured data from documents (e.g., email bodies, PDFs, and scanned documents) and converts them into structured data.

IDP “automates the processing of the data contained in a document – it understands what the document is about, what information it contains, extracts that information and sends it to the right place.”

IDP differs from Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR), older software that can convert a scanned image into text (i.e., scan a check at a bank) in that it not only captures but also extracts data from documents, categorizing and exporting the data. Relevant for further processing with AI technology.

IDP solutions are typically “non-invasive” and easy to integrate with existing systems, business applications and platforms. They also cover the gamut of ready-made solutions to more complex custom implementations.

Possible use cases are:

  • invoice processing
  • Digital document archiving
  • Insurance claim process
  • fraud detection
  • Case study review
  • contract management
  • Mortgage loan application processing
  • Customer engagement

Why do we need intelligent document processing?

Intelligent Document Processing Europe (IDP) offers a breakthrough method for automating data extraction tasks that were previously extremely difficult, if not impossible. It’s also important to know that IDP is more than just scanning invoices.

Today, the main benefit of IDP is the use of APIs and trained modules for some of the most famous documents, such as bank statements, contract forms, invoices, IRS forms, driver’s licenses, etc. This means you don’t have to spend a lot of time building models from scratch to practice again; with IDP, you can get started immediately.

IDPs’ various tools, APIs and modules also show missing values, fields, and duplicate records, reducing data redundancy, manual processing, and error rates. Once the IDP solution extracts reliable data, users only need to evaluate and approve the final update for the platform. Users can then bulk upload documents and process them for future use.

Therefore, it is unsurprising that IDP is also at the forefront of integrating artificial intelligence (AI) and machine learning (ML). It’s a great fit because you’re feeding more and more data, which is manually checked and verified. This means your custom implementation can become more innovative and accurate over time, resulting in more savings.

How does Intelligent Document Processing work?

First, the scanning hardware captures information from paper documents, converts them into electronic format, and provides digital versions of the documents as input to the IDP solution.

The computer vision algorithm in the IDP solution can recognize different document layouts from scanned images, intelligent document processing pdf files, and different file types, both digital and paper.

The Natural Language Processing (NLP) technology in IDP workflows can recognize characters, symbols, letters and numbers from paragraphs, tables or unstructured text in documents.

It synthesizes them using OCR and techniques such as named object recognition, sentiment analysis and feature-based tagging, successfully reading information from documents and entering content management systems with an accuracy of over 99%.

Following are the main steps in IDP workflow:-

  • Document pre-processing
  • Document classification
  • Data retrieval
  • Data validation
  • View data
  • Pre-processing of documents

Where there is data mining, there is OCR. When a document enters a document processing solution, it goes through the first phase of document pre-processing in the IDP workflow. The overall accuracy of OCR depends on how accurately OCR can identify/distinguish characters/words from the background. Some of the main techniques used in this phase are:

  • Binarization: In simple terms, binarization is converting color images into black and white pixels. Now the image consists of only 2 types of pixels – black pixel value = 0 & white pixel value = 256. Its purpose is to create a binary file and distinguish between the characters to be read in the text file (black pixels) and the background (white pixels).
  • Italic: When scanning a document, the scanned image may be slightly tilted horizontally, which is not ideal for OCR. Techniques such as the projection profile method, the Hough transform method, and the topline method are used to correct the distortion.
  • Noise Reduction: This step aims to remove unwanted small dots/spots so that OCR doesn’t mistake these dots for characters.
  • Classify the document

Document classification is done in 3 steps:-

  • Format identification: Find out if the file is a PDF, JPG, PNG, TIFF or another file format.
  • Structure identification: OCR solutions try to differentiate between structured, semi-structured and unstructured documents. Structured documents have a fixed template and layout, whereas semi-structured documents have several types of structure, meaning they can contain similar information in different places in the document.
  • An invoice is an excellent example of a semi-structured document – the supplier’s address on different invoices can be in different places. Document processing solutions need a contextual understanding of data and documents to understand these values.
  • Unstructured documents have almost no structure, but companies need to extract data from them for various purposes. In an unstructured document, it can happen that specific values don’t have a key associated with them.
  • For example, a date or email address can exist in the document without a critical identifier such as “date” or “email.” For example, a date or email address can exist in the document without a critical identifier such as “date” or “email.” For example, Contracts are good unstructured documents.
  • Identification of Document Type: The third step of document classification tries to understand the document type, i.e., whether the document taken is an invoice, bank statement, T12 statement, shipping label or another document.
  • The ability to successfully identify document types and queue them for data extraction depends on the data already entered into the IDP solution.
  • Data Extraction

There are two main parts of data mining:-

  • Retrieve Key-Value Pairs: Retrieve values assigned to unique key identifiers in documents
  • Table Extraction: Extraction of row elements arranged in tabular form
  • To do this, there are unique ways:-
    • OCR: It is the starting step of data extraction. As important as this step is, specific errors can occur during OCR:-
    • Word Recognition Error: Failed to recognize blocks of text in images, an error usually caused by the poor image quality.
    • Word Segmentation Error: Incorrect word interpretation due to error detection of word spacing, alignment and different text spacing.
    • Character Segmentation Error: The individual character in the segmented word could not be found. This is common with italics or serial letters.
    • Character Recognition Error: Failure to identify the correct character in the limited character image.

This error can be fixed by searching the dictionary, k-mer and n-gram language models.

  • Rules-Based Retrieval: The rule-based model works well for structured and semi-structured documents. This pattern can identify key-value pairs/row positions by referencing the positions in the document.

Named object recognition OCR and n-gram pattern recognition approaches help identify the values associated with key identifiers.

For example, regardless of the invoice number’s position in the invoice, the string next to “invoice number” or “invoice number” is the value the model is looking for.

  • Learning-based approach: Deep learning and ML-based OCR hybrid data mining techniques require supervised/unsupervised learning to train their models. The accuracy and confidence value determine the performance of this model.

As the number of documents processed and the amount of training and feedback provided increases, the accuracy of the model increases. Docomo uses a similar approach to data mining, where the ML-based model sits on top of pattern-based OCR.

Docomo uses a simple OCR correction approach and context-based NLP to improve data accuracy and quality.

Data Validation

This step is crucial to finding inaccuracies in the extracted data. Specific data validation rules are applied to the document so that any inaccuracies can be detected and flagged for correction.

For example, the “Total Amount Due” on the invoice must be the sum of the “Subtotal” and “Amount of Taxes Payable.” If there are discrepancies between the two, the invoice is flagged and held for review.

Human Verification

While we would like it, no data mining model is 100% accurate, so there is some human intervention in the IDP workflow. A human checks each document marked in red in a circle.

This is especially useful in supervised model training and improves model accuracy. The more processed and verified documents, the better the data mining model accuracy.

Once the data is extracted and cleaned, the software can send it to a database or export it in various formats. IDP workflows allow users to convert documents into formats like JSON, XML, PDF and more.

How Intelligent Document Processing Platforms Manage Each Stage of Document Data Integration

Three pillars of intelligent document processing Gartner

When it comes to processing documents in intelligent new ways, everything relies on three cornerstones: machine learning, optical character recognition, and robotic process automation. Let’s imagine an intelligent document processing Gartner as a living organism to understand better how magic happens. Thus, OCR can be seen as “eyes,” machine learning as “brains,” and RPA as “arms and legs.”

Optical character recognition, or OCR, is a narrow-focus technology that can recognize handwritten, typed, or printed text in a scanned image and convert it to a machine-readable format.

As a standalone solution, OCR only “sees” what’s in the document and extracts parts of the text from the image but doesn’t understand its meaning or context. That’s why “brains” are needed.

Machine learning is a field of knowledge that focuses on creating algorithms and models to learn from data to process new input and make their own decisions. IDP relies heavily on ML-based technologies such as

  1. Computer vision (CV) uses deep neural networks for image recognition. It identifies patterns in visual data – such as scanned documents – and classifies them accordingly.
  2. Natural Language Processing (NLP): which incorporates linguistic elements such as individual sentences, words, symbols, etc. into the document, interprets them and performs a linguistically sound summary of the document.

Robotic Process Automation (RPA) uses software bots (robots) to perform repetitive business tasks. The technology has proven effective in handling data presented in a structured format.

RPA software can be configured to collect information from specific sources, process and manipulate data, and communicate with other systems. Most importantly, because RPA bots tend to be rule-based, changes in the input structure prevent them from completing tasks.

Regarding connectivity, most IDP solutions are built on the RPA platform and involve OCR technology at various stages of the document processing cycle. This allows document-controlled processes to be fully automated.

Now that we’ve sorted everything out let’s take a step-by-step look at how intelligent document processing in the USA (United States Of America) works.

What are Intelligent document processing benefits?

Document processing can be a potent tool for organizations. It can automate specific tasks, make processes more efficient and improve document quality.

What are Intelligent document processing benefits?

Here are some of the intelligent document processing benefits:

  • Productivity Boost
  • Faster document retrieval
  • Improved Accuracy
  • Reduce manual effort
  • Improved process efficiency
  • Automatic document classification
  • Improved compliance
  • Processing large amounts of documents
  • Improve customer satisfaction
  • Security enhancement
  • Increased flexibility

What are the benefits of combining IDP and RPA?

Empower business users to automate all business processes involving the documents themselves. IDP is an integral part of intelligent automation. Automation is only possible to the extent that there is data to work with.

With standard RPA systems, setting up data mining to run automation is often a separate third-party project that incurs ongoing costs and integration bottlenecks.

Effective data extraction and information structuring is the gateway to automating the lion’s share of business processes that rely on manual input and intervention.

Business users can automate end-to-end processes by integrating intelligent document processing UK (United Kingdom) into the RPA platform. When IDP and RPA live together on the same platform, the most significant piece of the automation puzzle works effortlessly in sync.

  • Start data processing. Quickly: The integrated RPA native IDP tool is easy to set up and often 5-10x faster than other approaches.
  • Reduce your processing fees: AI-driven IDP+RPA improves Straight-Through-Processing (STP) through continuous learning from human feedback.
  • Business user-friendly: Integrated IDP makes it easy to start with selectable ready-to-use use cases for the most common document processing scenarios.
  • Enterprise level automation: Connect your IDP software to other parts of the business to deploy a fully integrated RPA system without the need for expensive upgrades
  • Powerful for developers: Improve document retrieval by tweaking your AI workflow with the ability to add custom logic (Python scripts).
  • Process any document: Accelerate digital transformation by combining the power of IDP, which can process structured and unstructured documents in almost any format with automation.
  • Safe and reliable work with documents: Quickly scale document processing operations and customize data collection to extract the data needed to get the job done
  • Self-improved document processing: Built-in AI enables more significant ROI over time as IDP bots continue to learn and improve
  • Plug and play data collection tool: Access to a broader range of tools such as dedicated OCR technology to support unique use cases
  • Use case library for extraction: IDP embedded in enterprise RPA software can be prepared with predefined extraction packages for immediate application to the most common document processing scenarios.

What are the Key elements of Intelligent Document Processing?

A number of core IDP technologies do wonders, saving organizations time and minimizing errors. Not all solutions cover all of these technologies, and their complexity varies.

  1. Image processing: The first step is to process the image of the received document (for example, by scanning or emailing). Computer vision algorithms enable image processing and prepare documents for OCR/ICR and optimal storage. IDP platforms typically generate two versions of digital documents: one for machine-reading and one for display on the screen in a content management system.
  2. Optical Character Recognition (OCR): OCR is a technology that translates scanned text images, either printed or typed and converts the scans into machine-coded text. This machine-coded text can be understood and used by other software and solutions, e.g., Robotic Process Automation (RPA) or billing software.

Improved OCR with few errors is essential for machines to read the text in documents (images). The use of a different OCR engine is one of the hallmarks of IDP. More complex solutions employ a multi-layered process that combines the strengths of several machines to achieve near-perfect accuracy.

  1. Intelligent Character Recognition (ICR): Intelligent Character Recognition is a more advanced type of OCR technology that is gaining popularity. The main difference is that ICR also understands different handwriting styles and fonts. ICR has evolved as technology advances to offer higher accuracy and detection rates.
  2. Natural Language Processing (NLP): An essential aspect of NLP is understanding and processing connections. This can be the context of labeling specific data (e.g., objects, names, functions), understanding synonyms (e.g., when the word bass refers to fish or sound frequencies), industry or language-specific terms and acronyms.

NLP looks for paragraphs, words, or other language components in your document that convey specific meanings. Using techniques such as sentiment analysis, deep learning, part-of-speech tagging, named object tagging, and feature-based tagging, NLP accelerates data discovery.

What’s the Difference Between Intelligent Document Processing and Data Capture?

The most significant difference between IDP and data capture is the innovation in document handling. Big names in traditional document capture stopped innovating more than a decade ago.

And there are two reasons for this:

  • First, these tools were developed when computer-assisted conservation was becoming critical. Their software architecture is not designed for the scalability required by today’s data-intensive applications.

And because many of these platforms have evolved through acquisitions, rebuilding software for the entire platform to meet IDP requirements will be very expensive.

  • The second reason is that the customer base for traditional document capture companies is significant. They are still profitable and don’t want to disrupt their customer’s workflow with the necessary upgrades.

Instead of innovating in the sensing field, they are focusing on developing other technologies such as robotic process automation or renaming them to give the impression that they have IDP capabilities (sad but accurate).

6 things you need to know about intelligent document processing:

  1. Eliminate the need for data document extraction templates
  2. Reduce document processing cycle times by order of magnitude
  3. Significantly improve the accuracy of your data document collection
  4. The scale at the speed of your business
  5. Find bugs and anomalies before they overload your system
  6. Work seamlessly with multiple document types across the company

Is intelligent document processing the same as OCR?

To know this, here is a brief layout that summarizes the differences between OCR and IDP:-

Key points IDP OCR
When should it be used? When working with complex documents such as images, tables, too many variants, or free-flowing documents. For simple structured documents that fit the template.
Other advantages besides data extraction IDPs understand data, context, insights, and generate narratives. Limited to data extraction only.
How is accuracy maintained afterwards? IDP uses machine learning techniques to understand and improve accuracy over time systematically. OCR is a manual process that needs to be set up with tools.
Does it need a template to work? IDP without templates. OCR uses templates that are expensive to create, maintain, and maintain.

How is intelligent document processing applied in logistics?

IDP ensures data is extracted, structured and verified. AI-based services such as OCR and NLP can be found in the IDP software, combined with the ability to automate PDF document processing. Intelligent document processing python follows the same process for converting unstructured and semi-structured data into structured information and vice versa.

Intelligent document processing United States Of America (USA) includes different phases starting with pre-processing, from capturing data in documents to validating and integrating this data into relevant systems. This includes collecting data such as date, time, location, title and other relevant information.

Intelligent document processing India (IDP), based on artificial intelligence (AI), machine learning (ML), and related technologies, can reliably read various types of documents that logistics companies accept for processing. Several industries can benefit from intelligent data mining and expand their data mining processes to streamline their processes.

AI and machine learning applications unlock actionable business data embedded in analog and digital documents in an accurate, cost-effective and highly scalable way. AI-based services allow you to change business outcomes throughout the document processing journey.

What are the B2B use cases for Intelligent document processing?

There are several B2B use cases that enable intelligent process automation, some of which are briefly described below:

  • Banking: Banking and lending is primarily a paper industry. For the most fundamental processes – opening an account or applying for a loan – countless documents and forms are filled out and signed daily.
  • Insurance: Insurance is another paper-heavy industry that can use intelligent computing software well enough to automate mundane office tasks. From filling out insurance forms to signing claim agreements, many things can be simplified with an IDP.
  • Health Care: Since the advent of digitization, many health care records have been uploaded to computers. In addition to reducing the new documentation and directly to the existing ERP. Intelligent document processing Europe is beneficial in healthcare here.
  • Logistics: Logistics is another industry that requires a lot of paperwork – invoices, inventory lists, fuel bills, fleet insurance documents, etc. High efficiencies can be achieved when office automation shifts the workforce to more strategically oriented tasks.
  • Real estate: Document sensitivity in the real estate industry requires efficient handling. Title Deed, Loan Document, Proof of Identity etc. – Many need to be validated before the process starts
  • Legal: If one industry relies entirely on documentation, it is the legal industry. The problem is with so many documents; it becomes difficult to search through them to extract the correct information.
  • Manufacturing: The manufacturing industry may have many different types of documents, and managing them can be very difficult without the power of intelligent automation.
  • Media: Contrary to what most people think, the media industry handles daily paperwork. Personnel lists, paper files for stories, printed matter for archives – a lot to sort through. Intelligent data processing helps streamline all these operations.
  • Human Resources: Human resources is another paper-intensive industry that relies heavily on printing materials for processing. That has to change – and intelligent data processing can help.
  • Government: Authority is filled with documents that need to be organized and sorted before they can be used.

Below are some detailed explanations:

How is intelligent document processing used in manufacturing?

The manufacturing industry has perhaps the most diverse document types, and managing them can be extremely difficult without the power of intelligent automation. Some of the use cases where intelligent document processing can be used successfully in the manufacturing industry are as follow:

  • Extracting information from invoices and organizing data into a neat array of defined templates for further processing is efficient and accurate and requires less time through intelligent data processing.
  • Intelligent data processing also facilitates cross-departmental coordination by extracting data from paper order forms, organizing them into digital templates, and directing information to appropriate departments for further processing without human intervention.
  • Each production unit must control the quality of all its products according to predetermined standards. Intelligent document processing United Kingdom (UK) helps extract data from these paper documents and structure them according to defined quality and assurance procedures to keep information connected and accessible.
  • The intelligent data processing means that order data coming in via multiple channels – fax, post or email – can be received automatically. The software then enters this data into the ERP according to the rules and protocols set by the administrator.

The manufacturing industry must automate its core processes and the workbench as much as possible to get maximum efficiency from operations.

How is intelligent document processing implemented in government?

Authorities are filled with documents that need to be organized and sorted before they can be used. How Intelligent Document Processing github Helps:-

Public and government survey results are made more effective with the help of intelligent data processing to analyze and report the results, highlighting key factors.

Application review and processing – such as RFPs or tender requests – can be automated to extract and process data for greater efficiency.

Intelligent software can be used wisely to analyze permission requests and send approval or disapproval.

What impact does intelligent document processing have on the insurance industry?

The insurance sector consists of many documents and complex documents to complete the whole process manually. It takes a lot of time and requires a lot of patience on both sides. Processing insurance claims and documents is tedious, but people traditionally have to complete the whole process.

The emergence of cutting-edge technologies, such as artificial intelligence, computer vision, machine learning, NLP, and especially automation, has turned the whole process into an easy, hassle-free, time-saving and cost-effective way. The transition to digital transformation has increased the demand for automation to replace everyday and mundane tasks with creativity in employees’ lives.

The insurance industry requires one person to manually cover 85% to 90% of documents due to intelligent document processing OCR (Optical Character Recognition) limitations. Intelligent document processing IDP solutions have only one challenge – complex and unstructured data automation.

Hybrid insurance offices must be entirely digitized with back office processes with the integration of intelligent IDP document processing solutions. Complex insurance claim documents contain complex layouts, poor handwriting, symbols, images, and more. In contrast, unstructured documents show variations over time, variations between sources, modified layouts, and changes modified with poor-quality images.

Intelligent document processing vendors help the insurance sector automate the extraction efficiency of traditional processes by automating the email attachment process in several steps – data extraction, categorization after identification, extraction in the proper format, and extraction from multiple sources.

This data is entered into the system after using artificial intelligence and machine learning to collect and identify categories of data effectively and efficiently. It also helps the insurance industry with the best customer experience. IDP’s intelligent document processing solutions can enable customers to scan and exchange specific documents in a more seamless process with automation and intelligent retrieval tools.

It has also attracted more potential customers to the insurance sector due to higher customer retention and follow-up services.

The productivity of the insurance industry can be increased by integrating intelligent document processing solutions into existing systems. There is a hybrid environment for unstructured data. If an intelligent document processing system cannot identify unstructured data, it will be passed on to employees who can better understand it. Thus, it improves operational efficiency and makes the whole process enjoyable.

Conclusion:

In today’s digital world, data management is vital to business success. The effectiveness of data management can impact the operating costs, efficiency of services and profits of any organization.

Companies using intelligent document processing platforms benefit from increased productivity, reduced costs, and targeted growth and expansion. IDP is a technology that can open new doors to success and prepare your business for the future.