Every organization wants accurate data to run its operations smoothly. Thus, we need to obtain data in an editable and structured format, but data entry without smart technology is a dead end. Time and manual efforts required for the task only make the process slower and inaccurate.
The most popular technology for data extraction from physical (unstructured) documents is Optical Character Recognition (OCR). OCR helps automate business processes by saving time, cost, and efforts needed to extract relevant data and store them as searchable texts for easy access.
How is Accuracy Rate in OCR Systems Measured?
Accuracy of any OCR related technology is measured by comparing the output with the source document. We count how many characters (or words) were recorded correctly out of the total characters (or words). For example if an OCR technology recognizes 950 out of 1000 characters correctly, then its accuracy rate will be 95%.
Moving Beyond Generic OCR
According to a study done by the U.S. Government Printing Office, OCR scanners have an accuracy rate between 90%-98%. They are even able to capture all the image data from older, discolored documents. This means in a page of 1000 characters, 980 to 990 characters can be accurate.
Unfortunately, this only holds for extracting the entire text from any image, but when it comes to interpreting the meaning of the extracted strings from non-standardized and free-flow documents like invoices, account statements, loan documents, etc. the technology falls miserably short. Moreover, traditional OCR systems provide output in a non-structured format, which leaves a lot of work for data entry employees. Both these issues can be addressed by using standard templates for such documents, but it’s beyond a company’s power to control the format and layouts/ text used in all documents e.g. invoices of its vendors. Hence we need a more efficient solution that can solve these issues without requiring a fixed template.
The accuracy rate is critical for unique documents in operations such as accounting, where inaccurate data can result in legal expenses, losses, and audit issues. Invoices, purchase orders, tax receipts heavily rely on extracting specific data and tagging it so it can be synced directly with accounting and ERP solutions.
While OCR scanning helps in digitization with a high success rate, enhancing the technology with Artificial Intelligence and Machine Learning means the system is now equipped to deal with documents in any template, language, or text style as well as returning the output in editable and structured (or semi-structured) format.
Experience More than 90% Accurate Data Extraction and Interpretation with OCR and Machine Learning
KlearStack relies on a self-learning technology for data extraction and interpretation that comes with continuous usage. That means the platform grows with you and adapts to your needs while working with you to optimize the data extraction and conversion process.
KlearStack is a platform that provides a 360-degree view and control of all your data needs. It is a template-free solution that digitally scans documents, analyzes with deep learning, extracts required data from specific fields, and helps you manage and monitor all the data through a dynamic dashboard.
With KlearStack, you are guaranteed to achieve 90% data interpretation accuracy in 90 days. By implementing KlearStack’s Intelligent Data Processing, you are entirely reducing human interference, reducing set-up costs by 80%, minimizing paper usage and storage, and increasing the productivity of your business processes by 200%.
Organizations that want to switch gears are adopting the AI-driven OCR technology (aka Intelligent Document Processing) for efficient data extraction, and KlearStack is a state-of-the-art platform that checks all the boxes when it comes to accuracy ratings and effective data capturing.
To know more about the SaaS platform and understand the concept of OCR technology in detail, simply click on the link below.
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.