Optical character recognition (OCR) technology has widely been touted as a game-changer; a miracle pill of sorts for organizations with a heavy reliance upon data entry and data capture processes. OCR automates the data entry process by scanning documents and images of text, interpreting and converting that data into searchable, editable alphanumeric characters.
For many, OCR software has proven to be a very useful tool that has largely cut humans out of the data capture process, freeing them to focus on more important higher-level tasks. But others may find that OCR just doesn’t meet expectations, delivering mixed results and poor ROI. What accounts for this differential in experience? It all comes down to the type of data that you’re processing and the precise capabilities of the OCR software that you’re using.
What Kind of Data Can OCR Software Process Effectively?
OCR software tends to do extremely well with certain forms of data, but accuracy and efficiency can drop off a cliff when presented with other data forms. This is what accounts for the differential in effectiveness and unfortunately, this point is one that many salesmen forget to mention.
To understand why OCR falls short in some situations, you need to understand the different types of data. They are as follows.
- Structured Data – Structured data is usually in tabular format, with rows and columns in a database, which can be easily extracted through SQL. This data type is easily analyzed and processed by OCR software.
- Unstructured Data – Unstructured data lacks a clear organizational model. Often, the data is in its raw form, without having undergone any sort of pre-processing. Unstructured data tends to be very difficult for OCR technology to process because it is erratic and free of consistent formatting.
In addition to struggling with semi-structured data and unstructured data, OCR software generally performs poorly with handwriting. Handwriting contains many variations and inconsistencies that make character matching difficult for a run-of-the-mill OCR platform.
There are also other factors that impact OCR accuracy, such as the quality of the source document. Background images, wrinkles, stains, faded ink and other flaws will affect the quality of output.
When an OCR software platform delivers subpar results, organizations are forced to rely upon human reviewers to go over the content for accuracy and, if necessary, perform corrections. In these cases, the cost benefits of OCR implementation become negligible because you’re still investing time and effort into the data capture process.
Not All OCR Software is Created Equal
There’s an old saying, “You get what you pay for,” and this is definitely true in the world of OCR software. Cheap, low-quality software generally delivers equally low-quality results, whereas pricier solutions tend to be more sophisticated and are capable of achieving far more accurate results.
When shopping for OCR software, it is important to remember that that objective is to save time and money. When opting for low-quality optical character recognition technology, any cost savings on the actual software may be offset if a human reviewer needs to spend a significant amount of time reviewing and correcting the captured data.
Bottom line: quality matters and any money that you “save” on the OCR software may very well be spent on correction man hours. For this reason, it is generally best to opt for a high-quality platform that is equipped with machine learning technology.
The Benefits of OCR Software With Machine Learning Capabilities
Traditional OCR software represents a rather simplistic, “dumb” form of automation whereby the platform is literally just character matching — often, with a relatively narrow threshold for what represents a “match.” This results in limited capabilities, especially when you introduce semi-structured or unstructured data into the equation.
But machine learning technology is a game-changer. Machine learning-enhanced OCR software can achieve great results for a broader range of data types. The OCR technology “reads” the data and the machine learning capabilities are used to translate and provide context to that data. The net effect is the ability to process semi-structured data and handwriting with a high degree of accuracy. This technology can even handle unstructured data.
As the name suggests, machine learning-enhanced OCR platforms can get more accurate over time. This is achieved in a few ways:
- The software can be “taught” to associate a broader range of shapes with a particular character.
- ML can be trained to “understand” what represents a data point and where that data can be found.
- Machine learning can be used to determine how a specific data point relates to other data points, ultimately providing better options for arriving at the results that the user is seeking.
Machine learning technology can dramatically improve OCR capabilities, increasing accuracy and ROI. At iTech, we are experts in helping our clients to make the most of their data. Contact the iTech team today to discuss how our solutions can help you use your data to its full potential.