OCR has been a buzzword in data processing since the past decade until IDP took over.
Intelligent document processing and Optical Character Recognition (OCR) are two important technologies that have revolutionized the way companies process and analyze data. While OCR has been around for decades, intelligent document processing is a relatively newer technology that is gaining popularity due to its ability to extract as well as analyze unstructured documents to transform them into usable insights in an efficient and accurate manner while actually processing the data. In this blog post, we will compare these two technologies from a technical perspective and look at various use cases for them.
Data Entry: The Last Milestone in Automation
Data is the building block of an establishment. It is pivotal to the organization’s progress and day-to-day workflow. But the biggest challenge companies face today, despite having an abundance of data, is harnessing this data from an unstructured pool of data dump and converting it into actionable insights.. Unfortunately, 80% of all business data is embedded in unstructured formats like business documents, emails, images and PDF documents. By 2025, IDC predicts worldwide data to exceed 175 zettabytes. With most of this information locked in emails, text, PDFs, and scanned documents — think for a moment, the volume of data in email alone — it poses a real barrier to automation and digital transformation.
The most widely used technology to overcome this issue is OCR. Let us dig into what it entails and to what extent has Industry accepted it.
Introduction to OCR
OCR is a legacy solution focused on extracting data from documents. OCR uses templates to constrain the extraction problem so it can increase accuracy. An OCR solution looks where its templates tell it to look on a page, and it recognizes characters. This approach is inherently tied to structured documents because it doesn’t tolerate variation well at all. When a document doesn’t fit an OCR template very well, accuracy plummets.
Optical Character Recognition (OCR) is an electronic conversion of the typed, handwritten or printed text images into machine-encoded text.
With OCR a huge number of paper-based documents, across multiple languages and formats can be digitized into machine-readable text that not only makes storage easier but also makes previously inaccessible data available to anyone at a click.
How does OCR work?
It usually involves three steps:
OCR software often “pre-process” images to boost the chances of recognition. Techniques for the same might include:
- Line removal
- Character isolation or “segmentation”
This is done to ensure that the best version of the images are used and most accurate representation of the text can be achieved.
Since the singular goal of OCR is extracting text from images or documents, it is important to earmark different features for alphabets and numbers.There are two main methods for extracting features in OCR: In the first method, the algorithm for feature detection defines a character by evaluating its lines and strokes.
In the second method, pattern recognition works by identifying the entire character.
We can recognize a line of text by searching for white pixel rows that have black pixels in between. Similarly, we can recognize where a character starts and finishes.
OCR accuracy can be improved if the output is limited by a lexicon (a list of words permitted in a document). For instance, this could be all the words in English, or a more technical lexicon for a particular field. Similar steps such as checking of grammar as well as error correction is undertaken to improve the accuracy of the final result.
OCR’s ability however is restricted to character recognition, companies often need to bundle it with human intervention. In addition, OCR cannot structure or process data, therefore, its ability has limitations that render OCR as insufficient for end to end automation.
IDP — The next frontier
IDP is an overarching technology suite that fulfills the enterprise requirement of last mile automation. IDP stands for Intelligent Document Processing. These solutions transform unstructured and semi-structured information into usable data. Business data is at the heart of digital transformation; unfortunately, 80% of all business data is embedded in unstructured formats like business documents, emails, images and PDF documents.
Intelligent document processing is the next generation of automation, able to capture, extract, and process data from a variety of document formats. It uses AI technologies such as natural language processing (NLP), Computer Vision, deep learning and machine learning (ML) to classify, categorize, and extract relevant information, and validate the extracted data.
This is important because using OCR requires multiple levels of human intervention and is not a fully machine driven process. In addition,OCR has limited value addition in digitizing data, not necessarily making the data actually work for you.
What does IDP achieve?
Businesses are under intense market pressure to operate more efficiently, deliver a superior customer experience, drive down costs, and comply with regulatory obligations. Increasingly, organizations are turning to intelligent document processing (IDP) to achieve these objectives.
Intelligent Document Processing (IDP) is an advanced technological capability that enables organizations to digitize and automate unstructured data originating from various documentation sources. These include digitized document images, pdfs, word processing files, online forms, and more. IDP uses technologies from machine learning, natural language processing, and workflow automation to mimic human abilities in identifying, contextualizing and processing documents.
IDP software enables organizations to digitize and automate their entire document processing function. It can be used to scan all manner of documents, identify pertinent data, extract it, organize and classify it, and ultimately transfer it into relevant back-office systems.
IDP software is able to make intelligent decisions on how to best manage and harness the data that it has previously extracted. It can do all of this in line with the production demands of an organization, scaling to meet greater documentation processing demand or reducing in kind too.
The machine learning component of IDP ensures that it continually improves the accuracy of the data it extracts and refines its performance accordingly. The right IDP software can produce extremely high accuracy on every file that it processes, improving compliance and efficiency, and also creating an audit trail of key data fields.
Urgent Need for IDP
Back in 2017, in an Intelligent Document Processing Automation article, McKinsey reported that organizations experimenting with AI were obtaining impressive results, including:
Automating 50–70 percent of tasks, which had translated into 20–35 percent annual run-rate cost efficiencies. Reduction of straight-through process time of 50–60 percent with return on investment (ROI) most often in triple-digit percentages.
In India, companies are seeing similar results. For example:
Achieved 95 percent manual effort reduction in Auto PII Redaction for one of India’s leading life insurance players by automating AADHAAR numbers mandatory masking as per the Supreme Court of India mandate. The first eight digits of all AADHAAR numbers, irrespective of location, are redacted out.
65 percent reduction in turnaround time in NBFCs within their lending divisioon for analysis of credit assessment related documents like bank statements, financial statements, KYC documents and associated documents for loan applications. In addition, there has been an average reallocation of 2 full time employees from this role to other roles due to the lack of need of manual intervention in data extraction and analysis.
Automating entire accounts payable activity by setting intelligent extraction, compliances and validation systems on invoices and e-way bills. Glib has been able to reduce process costs by 90% through this integration.
It is prudent therefore, to say that while OCR introduced the concept of digitizing data and making it usable, IDP has revolutionized the space by driving transformational change through the introduction of AI and ML technologies which can work with remarkable accuracy despite unstructured data sources and have made iend to end automation a real feasibility,