Why Do We Need Cognitive Data Capture?
Capturing data from business documents such as invoices, receipts, forms, delivery notes, customer records etc. has been a necessity since digital had become the new paradigm. This necessity stems out from the imbalance between the evolvement speed of technology and the evolvement speed of real life.
We are able to forecast annual production amount of a plant just in minutes or we can hire software robots to improve customer relations processes of a financial institution. Yet this doesn’t change the fact that we live in a world of paper documents.
Cognitive data capture is the way of closing the gap and hence providing a productive solution to companies for eliminating manual paperwork.
How Does Cognitive Data Capture Work?
Cognitive data capture, as the naming suggests, is a data capturing/extraction method which is based on neural networks to recognize patterns and relations within a document. This is, somehow imitating human mind by AI (artificial intelligence). Cognitive systems process documents without referring to a single layout or pre-defined and strict rules.
Cognitive data capture aims at reading documents with a contextual point of view. So that, just like humans, once the system grasps the context of the document (internal and external) it can recognize and classify any content involved.
Traditional OCR vs Cognitive Capture
OCR (optical character recognition) is an image to text technology, which is also used for document processing. It was originally developed for converting images of a textual content into digital units. Thus, whole content is easily transformed into a searchable and editable format. Think about e-books or PDF to Word applications. OCR scans the image and catches each light/dark patterns. Then it compares these patterns with a font database, and turns pixels into letters according to best match.
OCR is also used for data capture and therefore document processing. It is a reliable resource for static scenarios. If you are processing a single document type, let’s say an invoice layout which never changes, you can live with an OCR solution happily. For, once you set recognition rules and fix the layout OCR is reliable enough. However, this is not the case for 99% of the business world. There are tones of different document types and several variations of each document type. Shortcoming of OCR becomes apparent when variability of documents come into play.
What happens if a company needs to process tens of invoice layouts coming from different suppliers? Or what to do for an international company when it has to deal with variable legal and commercial documents, due to local governmental regulations? Only way to provide satisfactory solutions to such cases is deploying cognitive data capture with ML/AI features.
Cognitive Data Capture for Invoice Processing Automation
Invoice processing is one of the most time consuming and error-prone operations among daily business operations. Accounts payable teams, no matter the size of the company, struggle with manual data entry, PO matching, validation, approval and payment terms.
Good news is, it is possible to automate invoice processing with cognitive data capture. Nonetheless, it is not easy to implement a fully automated invoice processing. Because, invoice types show a great range of variety and the system has to posses very high accuracy rates in order to offer a reliable automation framework.
It is significant to employ an invoice automation solution, which has self-learning abilities and thus is document-agnostic and flexible. Using cognitive data capture, such a system is able to adapt itself to unfamiliar invoice types and execute the tasks without human intervention.
