What is Intelligent Document Processing? Why IDP is important in the business
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Paperwork is the lifeblood of many organizations. According to from one source, 15% of a company’s revenue is spent creating, managing and distributing paper documents. But documents are not only expensive, they are also time-consuming and error-prone. More than nine in ten employees respond to ABBY 2021 questionnaire said they waste up to eight hours a week searching through documents to find data, and traditional method creating a new document takes an average of three hours and makes six errors in punctuation, spelling, omissions, or printing.
Intelligent Document Processing (IDP) is being touted 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 natural language processing to digitize paper and electronic documents and extract data from that era – and analyze them too. For example, IDP can validate information in files such as invoices by comparing them with databases, lexicons and other digital data sources. The technology can also sort documents into different storage buckets to keep them up-to-date and better organized.
Due to the potential of IDP to reduce costs and free up employees for more meaningful work, interest is increasing. According to according to KBV research, the IDP solutions market could reach $4.1 billion by 2027, with a compound annual growth rate of 29.2% as of 2021.
Process documents with AI
Paper documents exist in every industry and business, no matter how ardently the industry or company has embraced digitalization. Whether for compliance, governance, or organizational reasons, enterprises use files for things like order tracking, records, purchase orders, statements, maintenance logs, employee onboarding, claims, proof of delivery, and more.
a 2016 Wakefield Research shows that 73% of “owners and decision makers” at companies with fewer than 500 employees print at least four times a day. As Randy Dazo, group director at InfoTrends, explained to CIO in a recent piece, employees use printing and scanning both for ad hoc processes (for example, because it is more “in the moment” to scan a receipt) and for “transactional” processes (such as part of a daily workflow in human resources, accounting and legal departments).
You cannot solve every processing problem with digitization alone. In a 2021 study published by PandaDoc, more than 90% of companies using digital files still found it difficult to create business proposals and HR documents.
The answer – or at least part of the answer – lies in IDP. IDP automates the processing of data in documents, meaning you understand what the document is about and what information it contains, extract that information and send it to the right place.
IDP platforms start by capturing data, often from different document types. The next step is to recognize and classify elements such as fields in forms, customer and company names, phone numbers, and signatures. Finally, the IDP platform validates and verifies the data — whether through rules, people in the loop, or both — before integrating it into a target system, such as customer relationship management software or enterprise resource planning.
Two ways IDP recognize data in documents are OCR and handwritten text recognition. Technologies that have been around for decades, OCR and handwritten text recognition attempt to capture important features in 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 the text) . letters).
When it comes to recognizing images or the content of images, computer vision comes into play. Computer vision algorithms are “trained” to spot patterns by “looking” at sets of data and learning the relationships between pieces of data over time. For example, a simple computer vision algorithm can learn to distinguish cats from dogs by including large databases of cat and dog photos with the captions “cat” and dog, respectively.
OCR, handwritten text recognition and computer vision are not flawless. Computer vision, in particular, is prone to biases that can affect its accuracy. But due to the relative predictability of documents (eg invoices and barcodes follow a certain format) they can perform well in IDP.
Other algorithms handle post-processing steps such as brightening and removing artifacts such as ink blots and smudges from files. As for text comprehension, it usually falls under the purview of natural language processing (NLP). Like computer vision systems, NLP systems grow in their understanding of text by looking at many examples. Examples come in the form of documents in training datasets, which contain terabytes to petabytes of data scraped from social media, Wikipedia, books, software hosting platforms such as GitHub, and other sources on the public web.
NLP-driven document processing allows employees to search for key texts in documents or highlight trends and changes in documents over time. Depending on how the technology is implemented, an IDP platform can cluster onboarding forms in a folder or automatically paste salary information into relevant tax PDFs.
The final stages of IDP may include robotic process automation (RPA), a technology that automates tasks traditionally done by a human using software robots that interact with business systems. These AI-powered robots can handle a variety of tasks, from moving files from database to database, to copying text from a document, pasting it into an email, and sending the message.
For example, with RPA, a company can automate report creation by having a software robot extract from several processed documents. Or they can eliminate duplicate entries in spreadsheets in different file formats and programs.
Growing IDP Platforms
Lured by the huge addressable market, a growing number of vendors are offering IDP solutions. While they don’t all take the same approach, they share the goal of abstracting archiving that would otherwise be performed by a human.
For example, Rossum offers an IDP platform that extracts data and makes 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 the fact that an invoice number can be on the top left of one invoice, but elsewhere in another.
Another IDP vendor, Zuva focuses on contract and document review, providing out-of-the-box trained models that can extract data points and present them in question-answer form. M-Files applies algorithms to the metadata of documents to create a structure, unify categories and keywords used within a company. Meanwhile, Indico ingests documents and does post-processing with models that can classify and compare text, as well as detect sentiments and phrases.
Among the tech giants, Microsoft uses IDP to extract knowledge from the emails, messages and documents of paying organizations into a knowledge base. Amazon Web Services’ Textract service can recognize scans, PDFs, and photos and feed any extracted data into other systems. For its part, Google hosts DocAI, a collection of AI-powered document parsers and tools available through an API.
How IDP makes the difference
Forty-two percent of knowledge workers say paper-based workflows make their day-to-day tasks less efficient, more expensive, and less productive. according to to IDC. And Foxit software 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 implement it’s not always easy. As KPMG analysts indicate in a report, companies risk not defining a clear strategy or actionable business goal, keeping people informed, and misjudging IDP’s technological capabilities. Companies operating in highly regulated industries may also need to take additional security measures or precautions when using IDP platforms.
Still, the technology promises to transform the way companies do business, and that’s important, while also saving money. “Semi-structured and unstructured documents can now be automated more quickly and accurately, leading to more satisfied customers,” Deloitte’s Lewis Walker writes† “As business leaders scale to gain competitive advantage in an era of automation, they must unlock higher-value opportunities by processing documents more efficiently and turning that information into deeper insights faster than ever before.”
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