Artificial intelligence is a word that is used frequently due to the rapid evolution of technology. AI is altering how we view contemporary technologies in a variety of industries. OCR, or optical character recognition, is one technique that has changed as a result of the use of AI.
OCR can transform text images into machine-readable formats after processing. It has the ability to digitize handwritten notes as well as printed materials. AI-based OCR has become essential for eKYC verification and digital onboarding.
Compared to traditional OCR, data capture is simpler with AI-powered OCR technologies. Grammar and punctuation errors can be checked for in the real world using AI-based OCR techniques. The output that is being provided matches the actual paper exactly. In this case, the AI also fixes any mistakes it discovers in the source document. In this case, the AI also fixes any mistakes it discovers in the source document.
As a result, people now utilise OCR for user authentication rather than just as a straightforward tool for basic translations. The user experience when migrating from the physical to the digital world can be enhanced by OCR tools.
Methodology of AI-based OCR:
A source image or document is scanned with traditional OCR to produce a text-based digital output. The accuracy of measurement varies based on the tool being used. While raw OCR programmers often only achieve an accuracy of approximately 71%, advanced OCR technologies typically achieve an accuracy of around 98%.
While these figures are reasonably accurate, IDcentral’s AI-based OCR technology elevates this implementation with a 98.6% accuracy rate. Let’s have a look at current implementations of AI-based OCR in the real world.
The scanned card will solely display the card details without any background noise or branding thanks to AI-based OCR capabilities. This makes it possible for companies to precisely scan and validate cards.
Finding out which texts in a document are bound in particular margins and lines is known as line detection. This aids in separating information that won’t relate to one another in the printed copy. They might even be more appropriate in other instances.
OCR divides several newspaper items without mistakes or muddles thanks to line detection. OCR is also used to weed out any extraneous information, including advertisement material.
Character extraction, where the tool detects each character, is frequently employed with handwriting. OCR associates it with the appropriate letter or character in the alphabet after identification. These characters are used to form their worried words and expressions.
The programme classifies and groups characters according to their pronunciation, spacing, and other characteristics.
Despite how accurate intelligent data models can be, mistakes can sometimes happen. After data collection, post-processing examines it for additional OCR tool faults.
Let’s examine some real-world uses for AI-based OCR now that we are aware of its applications.
AI-based OCR Tools: Business Applications
Businesses can utilise machines to convert text by combining OCR and AI technologies. OCR is also used to check for conversion-related mistakes. Additionally, based on specified criteria, AI may determine whether a document is authentic or fake. This can assist in identifying and stopping fraud before it harms firms.
OCR systems are used in retail to extract data from buy orders, invoices, and bills of lading. When used in conjunction with automated invoicing systems, OCR systems can also produce invoices.
When onboarding new customers in the banking industry, numerous manual forms have been used to collect user data. These forms can be taken and converted to a digital version for digital KYC checks using AI-based OCR. Customer data and identity verification services are also provided using AI-based OCR. This thwarts fraud and forgery efforts.
AI-based OCR can identify handwriting, validate documents, and uncover fraud and forgery efforts. This makes it possible for financial teams to identify fraud attempts far more quickly than would be possible using manual methods.
Address validation and identification verification are provided by AI-based OCR digital onboarding systems. These factors make AI-based OCR popular among banks’ eKYC systems.
AI-based In order to identify fraudulent claims, OCR digitalizes paper forms and claims and performs automated checks. Digital KYC verification enhances the customer onboarding process and the manner in which data is collected.
The implementations of AI-based OCR have not only shown promise, but they have also offered practical business applications that make them practical in the real world. But this is just the beginning, the ongoing advancements in the field of artificial intelligence, we can anticipate some exciting and practical applications of AI-based OCR.