Machine Learning Powers OCR’s Automation Evolution
iTech Data Services

Machine Learning Powers OCR’s Automation Evolution

16Jun
Read Time: 4 minutes

The world is collectively moving toward digitized documents, with paper documents becoming less and less commonplace as they’re scanned and converted into a digital format. But not all data is easy for these programs to “read,” while other data is structured in a way that’s just plain difficult to translate. But this is where optical character recognition (OCR) technology comes into play.

OCR solutions can be employed to scan and convert a document’s raw data into digital text that is searchable and easy to organize or analyze. But you can run into challenges because less sophisticated OCR software platforms lack the ability to analyze and interpret the data that has been scanned and digitized. These “dumb” OCR platforms really just scan in raw data that a human must then review in order to determine accuracy. Human intervention is also required if you plan to organize that data in any meaningful manner. The bottom line: people are tasked with reviewing the data to determine what is important and how that data ought to be treated. This is a rather cumbersome approach for generating a useful, digitized data set.

Enter: machine learning technology. Machine learning-powered OCR can transform a “dumb” simplistic optical character recognition software platform into an intelligent one that can process and organize data in a meaningful way.

How Does Machine Learning-Enhanced OCR Work?

Machine learning-enhanced OCR software represents a tremendous step up over traditional OCR technology. Machine learning adds a layer of context to the data that is scanned into the system, making it possible for the OCR scanner to accurately interpret a far broader variety of fonts and characters. This increases accuracy in a dramatic way.

But the benefits of OCR with machine learning capabilities goes far beyond improved accuracy. In fact, processes that require human intervention are largely eliminated when you add ML to the equation. Not only does this eliminate human error as a possibility, but it also frees those human resources so that they can focus on higher-level tasks and projects.

Machine learning-enhanced OCR can handle all three forms of data — structured, semi-structured and even unstructured data. That’s a far cry from traditional OCR scanners, which can only process structured data (and many of the more simplistic software programs may even encounter struggles with data that falls into the “structured data” classification.)

Here is a look at the basic capabilities of ML-driven OCR:

  • You can “teach” ML-enhanced OCR to associate a wide variety of shapes with a specific character, allowing for greater accuracy.
  • You can develop an algorithm that “understands” what constitutes a data point and where to find that specific data point.
  • You can use machine learning technology to evaluate how a particular data point relates to other data points. This contextualizes the data and allows for more comprehensive data analysis.

With the right inputs and “training,” machine learning mimics the human brain in terms of its ability to “understand” what data is important and what data is less relevant. Even better, machine learning technology can “learn” and improve its outputs over time, adapting its own algorithm to work more efficiently. At the end of the day, you have a technology that gets more effective over time, producing a better and better end result.

What Are the Benefits of Machine Learning OCR Software?

Machine learning is really driving the automation evolution, especially when it comes to technologies such as optical character recognition software. This is one type of tech that is really transformed when you add ML into the mix. Here is a look at the many benefits of opting for a machine learning-powered OCR scanner software platform.

Better Data Capture Accuracy – ML-driven OCR software allows for highly accurate data capture, especially when compared to its counterparts without machine learning capabilities. The machine learning algorithm can handle a broad variety of different characters and fonts, including handwriting and even images, resulting in extremely accurate data capture. What’s more, by automating the data capture process, you are effectively eliminating human error from the equation.

Improved Efficiency Over Time – As mentioned, machine learning algorithms have the ability to self-modify, resulting in improved automation, better accuracy and more advanced data capture capabilities.

Unmatched Data Capture Speed – Machine learning-powered OCR can capture data at a rapid pace that far exceeds what a human could achieve. And that actually dovetails with the next benefit surrounding the use of human resources.

Human Resources Are Freed Up to Focus on Other Tasks – Machine learning-enhanced OCR platforms effectively automate the data capture process, which means that human resources are freed up to focus on other projects. This is good news for productivity and it’s good news by way of accuracy since even the most accurate and experienced human data capture specialist would struggle to match the most basic machine learning-enhanced OCR software in terms of speed and accuracy.

Additionally, machine learning requires fewer and fewer human inputs over time thanks to its ability to improve and adapt. This means that this software becomes more and more hands-off as it processes an increasing number of documents.

Machine learning can have a dramatic impact on an OCR software system’s efficacy, accuracy and ROI. Acquiring this technology for yourself can be costly though — unless you have a trusted partner who has the sophisticated platform that you need to achieve meaningful OCR data capture. At iTech, data is our specialty and we are here to help you leverage your data to its full potential. Contact the iTech team today to discuss your OCR scanning and data capture needs.


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