Home Business What is Intelligent Document Processing? Why IDP is important in the enterprise

What is Intelligent Document Processing? Why IDP is important in the enterprise

by Uneeb Khan
Intelligent Document Processing

Paperwork is the lifeblood of many organizations. According to one source, 15% of a company’s revenue is spent creating, managing, and distributing paper documents.

 But documents aren’t just expensive – they waste pace and are error-prone. More than nine in 10 employees responding to an ABBY 2021 survey said they spend up to eight hours a week examining documents to find data and using the traditional method to create a new document takes an average of three hours and incurs six errors in punctuation, spelling, omissions, or printing.

Intelligent Document Processing (IDP) is presented as a solution to the problem of file management and orchestration. 

IDP combines technologies such as computer vision, optical character recognition (OCR), machine learning, and herbal language processing to scan paper and electronic documents, extract data from them, and analyze them. 

For example, the IDP can validate the information in files such as invoices by cross-referencing it with databases, lexicons, and other digital data sources. 

The technology can also sort documents into different storage compartments to keep them updated and better organized.

Because of the IDP’s potential to reduce costs and free up employees for more meaningful work, interest in it is growing.

 According to KBV research, the IDP solutions market could reach US$4.1 billion by 2027, increasing at a compound annual growth rate of 29.2% from 2021.

Document Processing with AI:

No matter how much the industry or company has embraced digitization, paper documents are plentiful in every industry and company. 

For compliance, governance, or organizational reasons, companies use files like order tracking, records, purchase orders, statements, maintenance logs, employee onboarding, claims, proof of delivery, and more.

A 2016 Wakefield research study shows that 73% of “owners and decision makers” at companies with fewer than 500 employees print at least four times daily.

Adopting digitization alone cannot solve all processing bottlenecks. In a 2021 study published by PandaDoc, over 90% of companies using digital files still find it difficult to create business proposals and HR documents.

The answer – or at least part of the answer – lies in the IDP. The IDP automates the processing of data contained in documents, which implies understanding what the document is about and the information it contains, extracting it, and sending it to the right place.

IDP platforms start with capturing data, often from various types of documents. The next step is the recognition and classification of elements such as fields in forms, customer and company names, phone numbers, and signatures. 

Finally, the IDP platform validates and verifies the data through rules, humans in the loop, or both before integrating it into a target system such as a customer relationship management or enterprise resource planning instrument.

Two ways the IDP recognizes data in documents are OCR and handwriting recognition. For decades, OCR and handwriting recognition technologies have tried to capture key features of text, glyphs, and images, such as global features that describe the text as a whole and local features that describe individual parts of the text (such as symmetry in letters).

Computer vision comes into play when it comes to recognizing images or the content of images. 

Computer vision algorithms are “trained” to recognize patterns by “observing” collections of data and learning, over pace, the relationships between the data. 

For example, a basic computer vision algorithm can learn to distinguish cats from dogs by ingesting large databases of pictures of cats and dogs labeled “cat” and dog, respectively.

OCR, handwriting recognition, and computer vision aren’t perfect. In explicit, computer vision is susceptible to biases affecting accuracy. 

But the relative predictability of documents (e.g. invoices and barcodes follow a certain format) allows them to perform well in the IDP.

Other algorithms handle post-processing steps such as lightening and removing artefacts such as ink smears and file smears.

 As for text comprehension, it is usually under the purview of herbal language processing (NLP). Like computer vision systems, NLP systems grow in understanding the text by looking at many examples. 

Examples come in the form of documents in training datasets, which contain terabytes to petabytes of data pulled from social media, Wikipedia, books, instrument hosting platforms like GitHub, and other sources on the public internet.

NLP-driven document processing can allow employees to search for key text in documents or highlight trends and changes in documents over pace. 

Depending on the technology’s implementation, an IDP platform can bundle onboarding forms into a folder or automatically paste salary information into relevant tax PDFs.

The final stages of the IDP may involve robotic process automation (RPA), a technology that automates tasks traditionally done by a human using instrument robots that interact with enterprise systems. 

These synthetic intelligence robots can handle many tasks, from moving files from database to database to copying text from a document, pasting it into an email, and sending the message.

With RPA, a company can, for example, automate the creation of reports by having an instrument robot extract different processed documents. Or they can eliminate duplicate entries in spreadsheets in various file formats and programs.

Growing IDP Platforms:

Attracted by the huge addressable market, many vendors are offering IDP solutions. While not everyone takes the same approach, they share the goal of abstracting archiving a human would otherwise perform.

Docbyte provides an IDP platform that extracts data while making corrections through what it calls “spatial OCR (Optical Character Recognition)”. 

The platform essentially learns to recognize different structures and patterns of different documents, such as that an invoice number might be in the awesome left corner of one invoice but somewhere else on another.

How the IDP makes a difference

Forty-two percent of knowledge workers say paper-based workflows make their daily tasks less efficient, more expensive, and less productive. according to the IDC. 

And the Foxit instrument reports that more than two-thirds of companies admit their need for paperless office processes has increased during the pandemic.

The benefits of IDP cannot be overstated. But implementing it is not always easy. As KPMG analysts point out in a report, companies risk not defining a clear strategy or actionable business objective, failing to keep humans in the loop, and misjudging the technological possibilities of the IDP. 

Companies operating in highly regulated industries may also take additional security measures or precautions when using IDP platforms.

Still, the technology promises to transform how companies do business – which is important while saving money.

 “Semi-structured and unstructured documents can now be automated faster and with greater accuracy, leading to happier customers,” Lewis Walker of Deloitte writes. “As business leaders scale to gain a competitive advantage in an automation-first technology, they will need to unlock greater value opportunities by processing documents more efficiently and turning that information into deeper insights faster than ever before.”

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