What is Optical Character Recognition and How Does AI Make it Better?
29 Aug 2020 Yogesh J
AI based OCR

Document Scanning Reaches New Heights with Optical Character Recognition and Artificial Intelligence

Optical Character Recognition or OCR is the technology used for recognizing text from printed or handwritten documents and converting them into digital format. In other words, it is a software that extracts data from scanned or photographed versions of physical documents and turns them into machine-readable text. 

 

OCR systems consist of a fusion of hardware and software that are used to convert physical data into digitally recognizable data. Equipment like the optical scanner is used to extract the text, and the artificial intelligence-enabled software takes care of the advanced processing. 

 

The technology uses artificial intelligence, which can handle complex tasks of identifying handwriting styles, fonts, languages, numbers, and even special symbols. 

 

OCR tech is widely used to convert physical copies of legal and financial documents into PDF or any other word processing formats. This saved data can then be accessed, edited, formatted, and searched quickly as a digital document.

How do OCR Scanners work?

For successful scanning, OCR technology uses pattern and feature recognition. Pattern recognition is used with pre-programmed or stored characters, where the scanner can recognize and interpret characters that it already knows via unique algorithms. Feature recognition, however, gives the technology a more advanced characteristic, where it breaks down the characters into features or lines and line direction, so it can identify even those texts that are not pre-programmed. When scanning is completed, the output is generated in standard text format that can be used for further manipulations. 

 

The technology helps to reduce human error to a great extent, but users are advised to go over some fundamental mistakes, template formatting, and simple proofreading for more effective documentation processing.

 

Apart from the financial and legal documentation processes, OCR technology can be used in many different places, like invoice and receipt processing, purchase order processing, license plate recognition, sorting letters/mail, organizing articles from old newspapers and magazines, and more. 

The digitization makes the documents more dynamic as compared to the physical version. This allows converting them into zipped files, highlighting words, quickly searching for keywords, and even adding them as attachments to emails. 

Revolutionizing Data Capturing: AI and NLP meet OCR

While OCR technology is capable of scanning and recognizing text, it is severely limited when it comes to interpreting that text. AI enriches this process by understanding the context of the text extracted by OCR. 

Today OCR tech is being revolutionized to suit different industries and that is made possible by combining it with Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML). These technologies give the data capturing software a much-needed boost, by enabling it to simultaneously capture information and comprehend the content. That means, you now have clarity on the context, and the AI-based OCR tools can now check for errors without human intervention. This combination is popularly called Intelligent Data Processing (IDP).

IDP provides users with information that can be tabulated; hence, it integrates perfectly with any other data system like CRMs, accounting systems, etc. Let’s take the example of invoice processing in financial and banking firms. IDP provides the OCR software with the intelligence to comprehend and organize text from different documents relating to transactions, trade policies, and statutory compliance policies. This data then gets synced with CRM and accounting systems to automate the entire process.

 

Why do Businesses Need Intelligent Data Processing? 

Businesses have to deal with thousands of documents on an everyday basis, and going through them manually is costly and tedious. Margins for error during the process can be eliminated by automating these systems. With Intelligent Data Processing (IDP) managing bulk documents has become accurate, time-effective, and speedy. 

Automating your business processes with IDP adds a layer of intelligence to your systems, specifically when dealing with high volumes of data that require to meet certain predetermined standards and policies. Complex data extraction methods can be burdensome and need some sort of training. Still, with software like KlearStack, users can expect accuracy as the end-to-end solution does all the data science behind the scenes.

With the current situation of the pandemic, all organizations must ensure that their employees are given a safe working environment. That means we need to reduce the need for in-office human personnel. IDP systems automate data capturing and structuring making it available for analysis at a rapid pace. Businesses can go beyond templates and focus on documentation that is accurate and in context. Thus, empowering the entire company and reducing the load on the accounts department at the same time. 

KlearStack is a template-free and is designed to tackle high-volume data interpretation in the invoice documentation processes. The pay-per-use model adopts AI tools for scanning invoices, purchase orders, expense receipts, and contracts to sort data into relevant fields for easier data management, and all this is done through Intelligent Document Processing. 

If you would like a free demo, then simply click the link below.

Conclusion :

It’s high time that organizations across all domains and irrespective of the organization size should adopt such AI based tools to reduce the wastage of resources and leakages in the cash-flow. 

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