What is Optical character recognition (OCR)?

Companies have collected data for a long time and with the modern world seeking complete digitization, converting physical data to digital is the most challenging factor. Also, many companies still ask for customer data through the physical application.

Is there a tool that helps companies with this job? Indeed! Optical Character Recognition is one such tool that is used to extract text from images and documents via electronic or mechanical channels. It converts typed, printed, or handwritten data into machine-encoded text which can then be used by a company to process different applications. Hence, a huge load of the manual data entry process is reduced by using this tool as a feature of Robotic Process Automation.

OCR an important component for automation

OCR technology has been around for more than a century. Initially, the software was trained to capture the limited font capturing an image of each character. But, integrating OCT with AI has given rise to a new world of physical data capture. With AI, OCR can capture data from machine printed documents and recognize handwritten patterns for many fonts and sized.

OCR with AI is a very challenging feature promising faster data capture and data entry with a high level of accuracy.

Why OCR is worthy in RPA?

Robotic Process Automation designs robots that replicate human activity that is repetitive and rule-based.

Most of the manual process involves reading and capturing data from physical or electronic documents. If we consider claim processing in Insurance domain, Account Opening in Banking domain, the recruitment process in HR domain or handling bill payments in Supply Chain, all their processes read a PDF or a Cheque or e-Document or a receipt, manually enter the data in the company database and process the requests as per the data entered.

Hence, using OCR in RPA will enable companies to automate such manual processes over a large volume of data. If we consider Feat Automation bot, we include surface connectivity capabilities that enable the bots to capture information from images, PDFs, or even remote applications. After capturing, the bots read the data and take related actions processing a request within a couple of minutes. If a person was made to read and enter data from 5000 different documents, it is a very tiring job and the person is bound to make mistakes. Whereas, replacing the person with a bot will make this activity provide a faster and accurate outcome.

The different types of OCR for RPA

OCR in business can be used widely for two predominant purposes:

  1. Scanning documents: Here, the OCR tool scans the document and collects unstructured data. Then, it converts the data into a structured, digitized format that can be easily fed into any business process applications. Such data can be further exploited in complex applications like CRM, ERP, or legacy systems.
  2. Reading Images from Remote Machines: the OCR tool solves a very complex problem of remote machines. Using surface connectivity to automate applications in remote machines, the OCR tool reads images and extracts necessary information from the screen image or simulation of the application. This is very advanced functionality that can benefit a company to expand its automation projects at a complex level.

Example of a working OCR in RPA

Time intensive tasks associated with manual processing of invoices into readable data is one of the very popular applications of using OCR engine alongside the RPA platform. Businesses will be happy to know that a sophisticated OCR solution can be fully integrated into complex business processes, seamlessly transitioning between Robotic data and human-supported input.

One challenge with scanning data through OCR is the constant threat of ‘suspicious data’. RPA process can be created such that any suspicious message will be immediately notified to a human using a callout screen through attended process automation. This way, a human can immediately check the information, verify and update the data thereby allowing the system to continue its automation.

If we have to illustrate the RPA OCR process, we can consider extracting information from a customer application and adding this data into the CRM system. In this process, a text analytics feature can be used to categorize the data, after which, the automation will be triggered by updating the CRM application. Also, any fault or discrepancy will be directly notified to humans to intervene.

Hence, the scope of RPA OCR is abundant in any business domain and it is a lot more effective and accurate process of data collection and interpretation with guaranteed reduction is time and money.

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