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 sizes.
OCR with AI is a very challenging feature promising faster data capture and data entry with a high level of accuracy.
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 the Insurance domain, Account Opening in Banking domain, the recruitment process in the 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.
OCR in business can be used widely for two predominant purposes:
Time intensive tasks associated with manual processing of invoices into readable data is one of the very popular applications of using the 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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