Data capture technology has advanced by leaps and bounds in recent years. What was once a fully manual process can now be fully automated, performing data capture processes with a high degree of accuracy and at a very rapid rate.
Optical character recognition or OCR technology has the ability to identify analog alphanumeric characters in an image or scan; the data is then rendered into a digital format that is both editable and queryable.
But OCR technology alone has its limitations. If the text takes the form of clear typeset content, traditional OCR software can capture the content with a fairly high degree of accuracy. But an OCR software program may struggle to identify characters, resulting in problems with accuracy. For this reason, developers have created a more sophisticated version of this technology — machine learning coupled OCR for data capture. But how does traditional OCR technology differ from machine learning coupled OCR, exactly?
Examining OCR Capabilities
While useful in some cases, traditional OCR is a rather “dumb” form of automation technology that uses a fairly simple process for data capture. The OCR software analyzes alphanumeric characters and then identifies those characters by comparing them to its database, which contains multiple conceptions of the various alphanumeric characters. It selects the closest match and then renders this character as an output in digital form. The software is essentially just matching the characters it “sees” to the characters in its database.
Data capture problems arise when there are imperfect conditions or variances in the characters that are being analyzed. These issues can occur due to many factors, including the following.
- Unique or complex fonts
- Script fonts
- Wrinkles or flaws on the paper / source document
- Faint, faded or imperfect typed text
- Font overlaying an image or graphic
These factors can render OCR software very inefficient for data capture, resulting in multiple errors that may take more time to review and correct than it would to simply capture the data manually.
OCR technology can also accommodate mapping, which allows the user to sort the captured data by field. But to achieve this, you need structured data.
- Unstructured data does not work well for OCR data mapping. This type of data is really best handled manually by a human.
- Semi-structured data can work with OCR technology, though a heavy amount of human intervention is required to achieve an accurate result.
- Structured forms of data bring the best results with traditional OCR software, though some human intervention is still required to achieve complete accuracy. The degree of intervention depends upon the quality and sophistication of the software.
Traditional OCR software virtually never provides a completely accurate output, which is problematic for obvious reasons. The solution: machine learning coupled OCR.
What is Machine Learning Coupled OCR? How Does it Compare to Traditional OCR?
Machine learning coupled OCR takes traditional OCR technology one step further by making it “smarter.” This results in more accurate data capture. Today’s most sophisticated platforms can achieve a remarkable degree of accuracy, especially over time as the machine learning algorithm adapts to recognize more and more characters.
As the name suggests, this technology combines optical character recognition capabilities with machine learning algorithms that are used to make the software increasingly efficient. Machine learning adds context to the data, allowing it to handle structured, semi-structured and unstructured data forms with a high degree of efficiency — you cannot say that for traditional OCR, which struggles with anything but structured data forms.
When “trained” properly, machine learning coupled OCR software can recognize the location and data type for any image of text that it encounters.
Machine learning capabilities also allow the OCR program to identify new versions of a character, which are then added into the platform’s database for future comparison. Gradually, the machine learning coupled OCR program “learns” to recognize a wider range of characters, allowing it to handle an increasing number of fonts / handwriting and less structured data forms.
In some cases, a small amount of human intervention may be required to approve the machine learning software’s suggested algorithm updates.. For example, the software may encounter an unfamiliar character that it “thinks” is an “L.” A human may need to review the character and confirm that yes, it is an “L,” at which point, the algorithm is updated to include this new conception of “L.” The most sophisticated machine learning coupled OCR platforms require almost no human intervention to this end, only needing human approval for difficult to differentiate characters (such as “I” and “1.”) Others get increasingly “confident” in their algorithm updates over time, thereby requiring less and less human involvement.
Overall, machine learning coupled OCR requires far, far less human intervention than its traditional counterpart, while simultaneously delivering a far higher quality of data capture. As such, this software solution represents a “smarter” and far truer form of automation. This is good news for organizations that opt to leverage this technology, since the entire objective of using OCR for data capture is to accurately capture information with little or no need for human intervention.
If you’re ready to learn more about machine learning coupled OCR technology, we invite you to contact the team at iTech. Our experts strive to help clients make the most of their data using today’s most innovative technologies. Contact iTech today to discuss how we can help you capture and leverage your data more effectively.