Data drives the economy, so it is no great wonder that organizations have a big appetite for it. However, collected data is useless until it has been read, and not all data is easy to read or structured in a way that is easy to translate.

This is where an Optical Character Recognition (OCR) solution is often employed. OCR digitizes semi-structured and unstructured data into an easily readable and editable format. This way, organizations can quickly analyze their information output and convert it into actionable insights. OCR is often deployed for invoice processing, document search, translations, sales order processing, etc.

Because of the considerable amount of raw data collected by organizations keeping up with the digital pace can be difficult. When employing a traditional OCR solution, they find that not only does it have difficulty when handling large data sets, it also contributes to already considerable workloads in addition to being time-consuming and error-prone. To overcome these limitations, Machine learning (ML) powered OCR has emerged to meet current digital landscape needs.

FOSTERING A NEW REALITY WITH OCR MACHINE LEARNING

Traditional OCR often requires human intervention for complete data capture and to ensure final outputs are error-free. These cumbersome processes are eliminated with Machine Learning, a type of Artificial Intelligence.

By introducing OCR Machine Learning algorithms, organizations can automate data entry, eliminate manual processing, and handle multiple data sets seamlessly in real-time; this creates minimized workloads, reduced processing times, and accurate error-free data outputs.

OCR Machine Learning also aids in the processing of a myriad of data types and languages. For languages, most traditional OCR solutions require individual translators for each language processed. Whereas, ML’s translation capabilities are all-in-one, allowing organizations to translate languages effortlessly in real-time.

A common example of this is Google translate which utilizes ML and provides a translation from any common language to any other common language within a matter of seconds.

Unlike traditional OCR, Machine Learning OCR “Learns”. If ML cannot interpret specific data sets a human can intervene to perform the validation. This has the added benefit of “teaching” ML how to handle this process should it ever encounter a similar instance. When it does, it simply follows the directions it was taught and performs the interpretation process automatically. It also learns pattern recognition and context.

Often ML can mimic people so well that an actual human cannot identify the difference between man and machine. Chat-bots used for online customer interactions are good examples of this. When used to capture data from handwritten documents, something OCR struggles with, ML often becomes so good at translating that it can also imitate it when needed.

The data collection goal of all organizations is to translate raw data into information and information into actionable insights. Machine Learning, unlike traditional OCR, does this with its ability to not just read, but to make logical decisions that put data into context.

Conclusion

ML’s ability to learn through example and pattern recognition gradually eliminate human error or intervention of any kind. Moving from manual or OCR to a Machine Learning OCR data management solution is the next step for all data-driven organizations.

iTech, as a data capture outsourcing partner, has a proven record in delivering best-fit services to clients. With a constant focus on innovation, iTech is an automation solutions provider for your Robotic Process Automation and Machine Learning OCR needs. Let us e-meet to talk more.

Reach out to our team today!

IDS Commander iTech2021

http://www.itechind.com