- August 5, 2020
- Posted by: Suhith Kumar
- Category: Text Analytics
The revolution is real. Long queues at railway ticket counters are a matter of the past. Airport check-ins are easier and faster, too, under normal circumstances. Few people now queue up outside cinema theaters to get tickets to watch movies starring their favorite movie stars. The fundamental reason: Optical Character Recognition (OCR).
You can now book a train ticket online and get it scanned and printed-out from the OCR kiosk at the railway station. OCR reader and passport scanner at airports capture personal and travel details from travellers’ passports and accelerate customs clearance by reducing the time waiting in queues for manual processing. It’s also used for scanning invoice and voucher codes on the packets of groceries and produce, saving precious time for customers and cashiers.
OCR is a technology that converts handwritten or printed text within digital images of physical documents into editable files which can be stored, searched, transferred, and sorted. OCR accuracy depends on the quality of the original document. For typed text, most platforms provide between 98 and 99 percent accuracy at page level.
Accelerated Invoice Processing
OCR, together with machine learning, is helping SMBs and enterprise-level organizations automate their invoice process. If it’s handled efficiently, it will save organizations plenty of time and reduce expenses.
A digital invoice, without OCR, needs manual data entry. While OCR extracts data from a digital file, it does not instinctively understand the invoice patterns to place data into the correct fields of an accounts payable (AP) system. This is where machine learning is involved.
OCR and machine learning combine to extract data and analyze an invoice’s structure for patterns, respectively. Together, they identify, for instance, the difference between an address and the due amount. The AP system helps place invoice data into their appropriate fields for processing.
Big data Efficiency in Banking
Optical Character Recognition helps optimize big data modeling by transforming paper and scanned image documents into machine readable and searchable PDF records.
The scanned document can then be integrated into a big data system that recognizes customer data from bank statements and other key documents.
Thus, OCR helps banks and financial institutions, which would otherwise have their employees manually examine image documents and enter inputs into a big data processing workflow, automate the process at the input phase of data mining. It also helps avoid ineffective and time-consuming manual data retrieval, freeing up time for employees to contribute to core business operations.
Reduce Banking Fraud and Storage Costs
Banks and financial institutions were among the first to use Optical Character Recognition and automation technologies.
Using OCR software’s capacity to recognize handwritten text, banks perform signature comparison to verify signed documents and identify possible forgery.
Customers, too, can leverage the technology to scan and deposit checks using their mobile phones, with a machine reading and processing details such as the account number, credit amount and signature.
According to a study by world’s leading professional services firm PriceWaterhouseCoopers (PwC), organizations spend $20 on average to file a single document, approximately $120 to find a misfiled document and an estimated $220 to recreate a lost document. One-fifth of an organization’s employees tasked with managing physical documents spend one-fifth of their time each week filing and retrieving documents.
By reducing paper records and leveraging OCR to convert scanned image documents into searchable PDFs, banks and organizations alleviate costs towards physical storage units.
Enhanced customer service
Customer service is the lifeblood of any business.
According to a research by New Voice Media, 49 percent of American customers changed companies in 2019 because of poor customer service. Another piece of stat that emphasizes the importance of customer service is 73 percent of customers fall in love with a brand because of friendly customer service representatives, according to data from RightNow.
OCR helps enhance an organization’s customer service thanks to the ease of data accessibility, which helps customer support personnel respond instantaneously to customers as they make an inquiry call or send an email. Fundamentally, swift responses improve customer satisfaction and win their loyalty.
Patient Record Digitization
Another sector that’s benefited from implementing Optical Character Recognition is healthcare.
Patients’ records containing key clinical data such as their medical history, conditions, inhibitions, and treatment plans are digitized and converted into structured data before being analyzed for real-time medical diagnosis, readmission analysis and treatment anomalies.
OCR in combination with Computer-Aided Diagnosis, helps identify critical medical conditions from digital records.
The technology helps in smooth retrieval using a search function while needing very little maintenance. The option to edit these records not only saves time but also helps when they need updating, which in the case of medical health records, is constant.
It’s almost impossible for businesses to go paperless, particularly for those who deal with legal contracts, mortgage filings, classified documentation and many more.
However, companies run the risk of data security if they are inundated with physical documents. Digital storage of data is not only safer but also user-friendly.
OCR helps organizations scan, store and analyze data digitally, thereby tightening data security. What’s more, access to digital documents could be minimized to prevent mismanagement of digitized data.
The demand for OCR solutions is on the rise, with the global recognition market size expected to reach US$13.38 billion by 2025, according to a report by Grand View Research. BFSI, healthcare and life sciences, education and legal sectors are among the prominent users of the software, which is a game-changer for organizations who deal with significant volumes of physical data but also to transform their business processes for the better.