Optical Character Recognition (OCR) is an AI-driven technology helping automate several business tasks. Current OCR scanning technology can discern the text and convert it into a digital file whether the corporation uses hand-written or printed documents. The outcome of OCR is additionally utilised for electronic document editing as well as compact storage and also forms the rationale for text-to-speech, cognitive computing, and machine translation technologies.
There are various kinds of OCRs depending on the tasks they decode:
- Intelligent Word Recognition (IWR) helps in the recognition of unconstrained handwritten words rather than individual characters.
- Intelligent Character Recognition (ICR), is a more refined form of optical character recognition as per modernising algorithms to gather additional data regarding deviations in hand-printed letters.
- Optical Mark Recognition (OMR) helps to identify the data that individuals mark on surveys and tests.
- Optical Word Recognition (OWR) scans typed documents term by term.
Financial transactions contain massive data entry. Manual processing of this information can take a lot of time and work whilst OCR processes make the digitisation of financial documentation and extraction of required data much smoother. As an outcome, OCR scanning technology enhances client onboarding and the overall experience.
Moreover, the utilisation of optical character recognition in the banking and financial sector includes:
Whatever financial transactions a client wants to execute, whether it be account opening, transferring or withdrawing of money, they first need to verify their identity. OCR scanning technology offers a completely automated onboarding approach consisting of scanning ID or passport and pulling the essential data using OCR text recognition. For instance, name, birth year, gender, image, signature, etc. Moreover, OCR services help cross-check whether the information matches the identity document.
Manual entry of payment details mostly contains errors and takes extra time than expected. The scan-to-pay feature employs OCR technology to quickly capture invoice information and automatically process it. The user only requires a smartphone to do this. OCR can also play the role of an additional safety element when making payments. Usually, users keep card details in apps, preferring not to enter the card number every time. With OCR, they need to turn on the OCR feature to extract data in seconds for new payments and then remove it.
OCR helps in extracting data from receipts for additional accounting or record analytics. Customers can find this feature incorporated in financial associate applications with money-tracking features for computerised data entry of payments and expenditure classifications.
The high variability and usually bad quality of tokens are the primary challenges for precise recognition with OCRs. In this scenario, the rule-based method cannot be sufficient, and this is where deep learning-powered OCR comes in. Deep learning technology allows the system to comprehend the acquired data and improve. Moreover, it allows training a prototype to determine Regions of Interest (RoI) in a picture that is more likely to have text, omitting redundant records such as the background.
ML text recognition tools and optical character recognition can boost mortgage applications as well as loan processing by up to 70%. Data entry automation makes the application review process as well as approval or rejection, more cost-friendly for the company. AI algorithms can analyse the needed data from apps to approve or disapprove based on the financial association’s rules.
Use cases of optical character recognition in finance are not restricted to the above. The technology can also process other financial documentation such as bills, financial reports, invoices, contracts, etc.
Optical Character Recognition (OCR) based on artificial intelligence and ML is a broadly employed technology for record digitisation and text recognition. Moreover, its use cases are expanding with the evolution of deep learning. Currently, one or another type of OCR scanning is playing an important role in finance, security, healthcare, retail, finance, communications, tourism, and other industries.
Additionally, business goals greatly influence the practices, architecture, and mechanisms that are required to develop OCR solutions. The data should coordinate with the objectives of every project and be as authentic as possible. Developing a practical OCR scanner app with ML is an easy thing, so companies should enlist the support of professional data science and artificial intelligence development units to get it right.