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Intelligent Data Capture: How Accurate is it?
With more and more organizations looking to automate their business processes, especially tasks like data entry, there is a widespread need for an intelligent platform that can help scan and convert text from physical documents into digitally accessible version.
Optical Character Recognition is a technology that is popularly used for such data extraction. OCR goes beyond the basic potential of document scanning by extracting character-level data from physical documents and converting them into editable documents.
But, not all documents follow the same templates, styles, and fonts. So how can the data extracted from these varied sources be accurate? According to research data obtained using AI-driven OCR technology is 90%-98% accurate even with documents that are discolored. That means reduced human interference, error rates, and costs incurred through incorrect data entries.
Accuracy of this data can be measured on the character level as well as the word level. When we say 90% accuracy, it means 90 out of 100 characters are accurate and ten characters are extracted incorrectly or are left undetermined.
How Can You Improve Accuray Rate?
Optical Character Recognition is executed in many steps, each having its own impact on the final accuracy rate. Here are some things you can do to improve your OCR accuracy rate:
1. Image Quality:
While the technology is capable of capturing data from lesser quality images, you will undoubtedly receive better results with images that adhere to the optimal image resolution. Some typical examples of bad image quality are – image/ photograph taken in a bad lighting condition, scanning a partial document, taking a photograph from a distance, blurred scanning, scanning at a low DPI, etc.
2. Manual Correction:
Even though we see a pretty accurate conversion, there is always room for improvement, and that comes with a manual check. Simple proofreading to check for basic grammar, spellings of lesser-used nouns, and relevant data capture improves your accuracy rate significantly.
3. Document Font:
While technology can identify different fonts, it is known to reduce the accuracy rate if the font is illegible or extremely stylized. Stylized custom fonts, handwriting, and artsy typefaces should be avoided. If a document of that sort is used, manual correction is definitely advised.
4. Learning Over Time:
Manual correction alone will only ensure accuracy with repeated human efforts. Hence, it is advised to use OCR solutions that use machine learning to learn from past corrections and improve accuracy rate over time. This means that even if the accuracy rate is just 75% in the beginning it can go close to 99% as the solution understands the nature of documents.
When to Use Specialized OCR Solutions:
Not all companies have the same requirements when it comes to data extraction. That’s why it is important to find a platform that can help you with your specific needs for a better accuracy rate. For example, when dealing with high volumes of invoices, a model that is designed to specifically extract structured data like invoice number, list of transactions, or address of the vendor, will give you a better accuracy rate.
How Can We Help?
KlearStack’s intelligent document processing is designed to resolve all the data extraction and interpretation needs of your accounts department. The platform is enhanced by AI and ML that helps you go template-free. KlearStack works on a pay-per-use model that can even perform line-item level extractions from invoices, receipts, and purchase orders. It can also sort data into relevant fields. You can monitor and manage all your data on an easy-to-use dashboard, where you’ll find an in-depth analysis of your captured data.
To know more about OCR and how KlearStack can help you with automating your business processes, simply click here
Conclusion :
It’s high time that organizations across all domains and irrespective of the organization size should adopt such AI based tools to reduce the wastage of resources and leakages in the cash-flow.
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