Does Intelligent Document Processing Mean Uprooting OCR-based Document Scanning?

Does Intelligent Document Processing Mean Uprooting OCR-based Document Scanning?

IDP (Intelligent Document Processing) and OCR (Optical Character Recognition) are two connected terms, carrying different meaning and purpose of existence.

With industry leaders capitalizing on latest technologies like IDP, there’s a common point of debate floating around in the industry – Will IDP uproot OCR-based document scanning?

Or let’s put it across the other way – Is IDP a replacement of OCR scanning?

To put simply, NO!

IDP and OCR scanning refer to two different aspects of document processing.

IDP is a smart data extraction solution that comprehends and captures unstructured and semi-structured data from a variety of documents and converts it into insightful, analyzable data sets.

On the other hand, OCR is a data capture technology that converts text from typed, printed, or hand-written documents into machine-encoded text or digital format. While OCR was meant to reduce paper-based document processing, IDP has emerged to improve OCR-based document processing.

IDP will definitely not replace/uproot OCR-based scanning, but eliminate the limitations of the latter. Some of the common instances of confined OCR scanning include delayed document processing, erroneous data extraction often leading to employee expense frauds, approval chases, and many others that impact business reputation.

Every business process requires OCR technology to initiate their document processing tasks. Irrespective of the type of documents, OCR tools help organizations streamline their data extraction process. While there are limitations with traditional OCRs, technologies like RPA (Robotic Process Automation) and AI (Artificial Intelligence) fill in the gaps to improve the efficiency, accuracy, and cost-effectiveness of OCRs.

Take a look at typical document types that require a combination of OCRs and intelligent document processing tools for optimizing business processes.

 

1. Pre-defined standard documents

Documents like compliance and regulatory forms, including tax forms and w-2 forms have pre-defined standards and formats that template-based OCRs can easily read and capture up to 98% of accurate data from them. Here, OCR-based document scanning combined with RPA tools is implemented to read, classify, and store these standard documents into sets of structured data without putting in much effort.

 

2. Free-form Semi-structured documents

The next set of documents include content in free-text, including paragraphs, bullets, text fields, etc. Some common examples of these types of documents are invoices, receipts, loan approval documents, purchase orders, and more. Organizations dealing with such type of data are not limited to handling just hundred or thousand documents.

Millions of documents need to be processed every month and organizations cannot succeed without OCRs. The technology helps in streamlining tasks like bulk invoice processing, loan processing, legal document processing and auditing, and medical records and diagnostics report processing, to name a few.

Moreover, when RPA and AI integrate with OCRs, they can handle deductive reasoning from the extracted data, leading to enhanced automation. That said, together these technologies reduce human intervention in mundane tasks to a great extent.

 

3. Unstructured documents

The most common set of documents every company struggles to handle are non-structured or unstructured documents, including handwritten text, slang, regional words, abbreviations, hashtags, or short-hand writing.

Such documents require significant comprehending of documents and understanding of the text before extraction. Hence, processing of these documents depend on OCR tools, NLP (Natural Language Processing) engines, ML (Machine Learning) algorithms, RPA, and AI for meaningful and intelligent data extraction.

Apart from the OCR technology that graduates from basic to advanced level, the degree of human intervention involved in the entire document processing task is almost negligible. While OCRs automate mundane activities, RPA and AI work together to eliminate mistakes and limitations of OCRs, thereby freeing up the “human” resources to focus on their core competencies.

 

KlearStack passes all the tests for being an OCR-based document scanning software, AI-enabled data extraction software!

We have already contemplated the automation industry before we started developing a full-fledged data extraction software.

While intelligent document processing is on the rise, it needs OCR as its foundation. Hence, we came up with KlearStack, a combination of template-less, end-to-end automated, and document decision supportive technology. It harnesses the benefits of both OCR and IDP to contextually understand and interpret data before extraction and convert it into structured data sets that companies can quickly put through analysis and decision making.

KlearStack had been serving clients across industries, including finance (trade and consumer durable) and accounting, and employee reimbursement claims frauds detection, to name a few. However, it is not limited to an invoice or receipt data extraction software. The technology expands its horizons beyond OCR and IDP to help businesses scale through their internal processes.

To know more about how KlearStack caters to variable business requirements, book a free consultation call with us today.

Ashutosh Saitwal
Ashutosh Saitwal
www.klearstack.com/

Ashutosh is the founder and director of the award winning KlearStack AI platform. You can catch him speaking at NASSCOM events around the world where he speaks and is an evangelist for RPA, AI, Machine Learning and Intelligent Document Processing.