Having an electronic version of a document is great. It’s portable, virtually indestructible, easy to store, and can be sent over the internet. However, companies must be able to go beyond these benefits and actually use it to drive their businesses. For example, if an insurance company has the capability to analyze contracts and gain insights about risk exposure, then that is one business benefit worth having. This is a clear business use case for OCR. The world OCR market is expected to grow over 16% over 2021 to 2028 and reach US$26.31 billion by 2028!
Hurdles to OCR
Insurance is not the only industry that needs OCR. It is of particular relevance in the FinTech industry, where on-the-spot loans and financing solutions are being made available to any user who qualifies for credit, and in the up and coming crypto industry, registering a lot of new users for trading. Documents need to be submitted which verify the identity of each user. Each document must be compliant with government regulations specific to the region, to qualify customers and such verification for each customer must happen in a matter of seconds!
Another hurdle to traditional OCR is the presence of so many languages and associated fonts and associated directions of writing. From Hebrew and Arabic which has its characteristic set of fonts and is written from right to left to English which is written from left to right and again with its own set of fonts, every language presents a unique challenge to OCR. Every OCR system must be adequately trained before the extraction process can begin.
Last and the biggest hurdle of them all is people trying to cheat their way in, be it illegal trading on Crypto platforms, a fraud credit card application or even medical identity theft to avail services from another policyholder. Take the instance of Vietnam. Its shift from a centrally planned to a market economy saw its economy improve. However, the fraud rate is as high as 52%, the highest in Southeast Asia. OCR technology, together with allied technologies such as facial recognition and other biometrics can help lower the incidence of frauds in countries like these.
How AI-powered OCR can help overcome these hurdles
Let’s look at the OCR solution itself now. OCR solutions in the last century were mostly template-based. In fact it was only in 2013, that the shift towards AI-based OCR happened. This means that it is no longer necessary to input the exact location of every character and define individual rules to extract it. That was too costly and time consuming. In an advanced machine-learning system, the algorithms are so spot on that 50 samples of data is what it takes for OCR to extract details from any given document at 95% accuracy. With a purely template-based OCR system, the accuracy goes down to as low as 60%.
Why HyperVerge for OCR?
But who will provide this reliable and error-free OCR solution? Unfortunately even with giant leaps in cloud computing and AI, it can still be difficult to find a reliable service provider. Enter HyperVerge. HyperVerge began its ‘journey of identification’ in 2017, when it first began to offer its facial and ID recognition services to B2B enterprises. HyperVerge is in fact today one of the top five pioneers in the world when it comes to machine-learning and AI-based OCR and facial recognition. In fact, in an age of pin point accuracy, where it is ‘no longer human to err’, HyperVerge has now built very capable and error-free AI-based biometric systems in-house.
Also, with an AI/ML-backed OCR solution as robust as HyperVerge’s, it is able to adapt easily to a new geography. HyperVerge is able to identify characters written in over 150 languages in nearly all of the half a million fonts present worldwide. Add the incredible capability to extract details from a document over limited connectivity from low-resolution and poorly contrasting images, and HyperVerge is clearly the winner.
How does HyperVerge train its AI for OCR?
Now what is the process like? Let us consider the case of a FinTech company that wants to onboard customers instantaneously on their shiny new loan services platform. Samples of each document type (driving license, voter ID, passport etc.) that the customer would be assessed on would be submitted to HyperVerge. HyperVerge’s AI-powered OCR system will then use these samples to determine the fields in the documents submitted to extract text from. This text will be relevant to making a unique identification. The documents may be structured or unstructured, but with a deep learning model that almost nearly mimics the human brain when it comes to analyzing these documents, accurate discoveries can be made.
How quickly can HyperVerge OCR adapt to a change of scenery?
And what is the timeline like? It would take HyperVerge’s inhouse team less than a week to go live in a new geography with their OCR solution. HyperVerge has been in the OCR space for the past 5 to 7 years and presently supports OCR for B2Bs across the world, with the capability to extend its service to several more. The ability to scale is HyperVerge’s greatest strength. The unique architecture of its AI models allow it to serve thousands of customers at a time, allowing companies to onboard several millions, and reducing customer dropoff.
In the near future, the accuracy of HyperVerge OCR is only set to improve. To stay on top of its game, HyperVerge continuously benchmarks against several top-ranked competitors, constantly updating its AI models. This has also helped, over the years, reduce the OCR errors that may have been present in older versions of HyperVerge’s own OCR software. The best companies in the world are those that better themselves over time, and HyperVerge follows the same paradigm, when it comes to the accuracy of its OCR solutions. Through benchmarking and continuous document training, HyperVerge stays ahead of competition.