What is Optical Character Recognition (OCR)?
OCR is an acronym for Optical Character Recognition, a technology that helps recognize different types of characters, including handwritten and printed ones. The technology not only recognizes these characters but also converts them into a digital data format. The only limitation of the tool is that it simply converts characters into digital output without understanding them.
How does Optical Character Recognition Work?
There are three basic steps when converting data into an electronic format using OCR. These include image pre-processing; character recognition; and output post-processing.
Step 1: Pre-Processing Image
Before recognizing the character, OCR software usually pre-processes images to enhance their quality for capturing data accurately. It removes or minimizes unwanted distortions and enhances specific image features.
Step 2: Character Recognition
To recognize characters and increase the performance of data processing, the tool leverages feature extraction capability. It comes in handy when the input data is too large to process. The algorithm only recognizes the main features while ignoring the ones suspected to be redundant.
Step 3: Post-Processing in OCR
For ensuring higher accuracy of OCR, post-processing is a popular error correction technique. It usually includes restricting the output by a lexicon so that the tool can identify the right words, numbers, and codes. It is especially useful when identifying lengthy numbers and letters like serial numbers.
OCR has been used in different forms for more than a few decades, but unlike its previous versions, artificial intelligence (AI)-enabled OCR tools can process data with high levels of accuracy. Their ability to accurately recognize handwritten notes is also drastically improving.
Another way to look at document processing with OCR includes five main steps:
- Identify: the document type: image, scanned handwritten document, or machine-readable text.
- Classify: based on the document type, classify it into various formats like invoices, timesheets, etc.
- Read: recognize the character in the document.
- Interpret: infer conclusions based on the recognized content in the document
- Act: perform different actions such as setting up reminders, sending a notification, etc. depending upon the interpretation of the data.
Several document types can use OCR to process data. These can be broadly classified into:
- Letters from customers
- Tax Forms
- Legal Bills of Cost
- Medical Coding/Transcription
- Claim Processing
- Policy underwriting and issuance
What is the value of OCR in RPA?
Optical Character Recognition in Robotic Process Automation (RPA) allows organizations to automate a large volume of operational processes that involve scanned paperwork or handwritten invoices/notes. When OCR is combined it RPA, it enables the software bots to extract information from images, remote applications, and PDFs and perform the desired actions such as summarizing it in spreadsheets. The result is higher accuracy, speed, quality textual output, allowing bots to automate more tasks.
Two Types of OCR for RPA
OCR technology can be used in two ways within RPA solutions. The first involves converting unstructured data from documents into more structured, digitized data. The solution reads across multiple scanned documents such as invoices and transfers the information into various applications like ERP, CRM, and legacy systems.
Watch OCR and RPA Bot in Action
The second includes complex automation capabilities such as using surface connectivity to automate applications off remote machines. This can be implemented to read images, recognize the necessary text, or simulation of the application.
RPA OCR Use Cases
Optical Character Recognition tools have become popular as most enterprises eye automation and digitization of their processes. OCR and RPA technologies automate time-invasive tasks that require manual processing of scanned documents into readable data. Companies can fully integrate a sophisticated OCR tool into the workflow of business process automation, requiring human intervention only if required.
Advanced OCR solution can also flag exceptions in data and send it to humans to verify it. This technology sends the information via a call-out screen through an attended RPA bot. Humans, then, need to check and validate the data and submit it into the system to complete the automated task.
A practical example of combined OCR and RPA is Know Your Customer verifications in banks. The combined solution can validate the customer’s identity through their identification cards. If there is any discrepancy between the customer’s data on his/her ID and the application form, it is referred to the human workforce. AI-enabled robots can also learn from human behavior, enabling superior accuracy and proficiency to handle similar exceptions in the future.
Towards smarter RPA solutions
As RPA transforms into cognitive automation and other sophisticated features, which can perform more complex tasks, advanced OCR will increasingly become a popular feature of any enterprise-grade platform. It will help companies save precious man-hours and provide accurate data by largely mimicking human capabilities. To know more about the combined capabilities of OCR and RPA, contact us today. Our team of experts will show you how these technologies can handle unstructured data at scale.