What do people do when a physical document needs to be digitized? Scan it, of course!

But scanning using standard equipment has its limitations. You can scan a document and convert it into an image or doc file, but you can’t edit or manipulate the data in any way once it is digitized. If you catch errors, you’ll have to go back to creating a rectified physical document and then re-scan.

Now imagine going through this process with high volumes of data and the paperwork that follows them. Not only will these loads of paper create confusion, but they will also reduce efficiency, increase costs needed to store them properly, and consume manhours that can be used elsewhere.

Once the paper documents are scanned, if you want to capture data in an editable format, you need Optical Character Recognition (OCR). OCR technology provides a much more sophisticated output compared to scanning as instead of capturing the document as an image, it identifies the characters and converts them into machine-readable text. This process allows you to edit the text, search for keywords, and retrieve information faster.

OCR document scanners are widely used as data entry solutions for documents like bank statements, computerized receipts,  invoices, and purchase orders, etc. OCR, by itself, has constraints when it comes to advanced data capture and interpretation. It can effectively convert text from physical paperwork into editable text. Still, it cannot provide an intelligent mapping of that data. Thus the final output is unstructured and requires manual man-hours if you need your data in a structured format. We need Artificial Intelligence and Natural Language Processing (NLP) to help advance the technology.

That’s where Intelligent Data Processing (IDP) comes in. IDP works with Neural Networks, OCR, AI, Natural Language Processing, and Machine Learning to make systems produce advanced analytics for more accurate data extraction.