Cognitive OCR — Is your character recognition software doing its best?
Even since the 80s, we’ve been scanning documents into digital systems and making sense of them. So, optical character recognition (OCR) isn’t exactly new or exciting technology. OCR is, simply, a way to convert images of text into machine-encoded text. Say, a PDF, a scanned image or a photograph of a document can be put through an optical character reader to receive information as text, which is far easier to manage, analyse or process.
OCR remains the most used way in which documents are processed across the globe. Both small business and multi-nationals alike process forms, invoices, purchase orders etc. in PDF or paper format, necessitating OCR to digitise them.
The most common use of OCR is when an entire page is scanned and converted into text that can be edited in a program like a notepad. For instance, when vendor invoices come in, an OCR would convert the paper invoices into editable text documents. From there, the accountant would have to verify for accuracy and then copy and paste information into the invoice processing system.
Zonal OCR makes it simpler, as it can scan specific areas of the document, which can be understood as a certain entry. Say, the number at the right-hand top corner can be understood as the invoice number. However, enforcing this kind of standardisation across all incoming invoices can be next to impossible.
Considering the breadth and depth of impact a better OCR solution can have, someone must be surely working on it, eh?
Yes, they are. And we are too. The latest in OCR technology is what’s being widely called cognitive OCR — technology that combines modern artificial intelligence (AI) and age-old OCR. Today, cognitive OCR, in smart collaboration with robotic process automation (RPA) technologies can achieve a lot more.
Scanning, indexing, storage, management, alerts and archiving can all be automated with cognitive OCR and RPA without the slightest manual intervention.
For starters, cognitive OCR scans better. It is able to scan the entire document for text such as ‘invoice number’ or ‘tax’ and understand that the text immediately next to it is related. From there, the RPA software can take over, tag specific inputs, update relevant applications and continue the process eliminating bottlenecks.
It can also help with proper indexing and management. Cognitive OCR can index documents based on various factors including time, location, user or even elements within the document scanned. RPA can understand the contents of the document and tag them as invoices, purchase orders, contracts etc. Bots can then make sure they are stored in the right place, alert the right people and trigger any other necessary action.
This process is most often smooth until it hits a file that is unreadable. Most cognitive OCR solutions today stop when any part of a document is sketchy. The solution rejects the entire document, which someone needs to manually take care of.
JiffyRPA, however, is smarter than that. When you scan a document, if the OCR is unable to read one particular field, it can process all the remaining information and leave manual intervention only for the part that is missing. This saves a significant amount of time for users.
Moreover, cognitive RPA systems can watch and learn from users. So, even though there might be a need for manual intervention — albeit minimally — it is just a one-time effort to train the bot. It learns and grows as the organisational processes evolve, making the long-term impact significant.
If you have people assigned to scan documents, enter information or validate machine-recorded information, you’re wasting precious time and energy of your people. You need a better machine. Think about it.
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