Extracting data from documents, or reviewing of data, has been one of the capabilities most sought after by companies. They are searching for a solution in the technology industry and the AI world-now more than ever.
This is something we’ve worked really closely with a lot of clients in different sectors and different use cases. The most important question that is often raised when offering a solution is how anyone will use the “solution” to extract data from their company records to automate their business processes. The response is-perhaps, but not a single size fits all. We’ll discuss different things that you need to remember when looking at the usage of document data extraction cases and discuss some concrete examples.
The first thing you need to do when looking at interpreting documents or using extraction cases for documents is to consider the type of document you are dealing with. Are those documents not organized or not? What file format are they? PDFs, JPEG, PNG, Word documents, etc. Whether the form is organized as a PDF or a digital Word document, is a template used, is there a restricted collection of formats or is there a wide or infinite array of numbers? When they scan PDF or image files, are the pictures clear, with the same quality orientation?
The document contains many significant differences and permutations, and above are some of the most important aspects to look for. These variables will depend on the method you take to incorporate strong solutions. Again, there isn’t a size that suits anything.
As far as invoice management is concerned, you are a shipping department that attempts to verify invoices or to buy orders from various suppliers, all with different formats and models. Such images are mainly scanned (converted to PDF) and have no flawless orientation or cosmetics. Your technical objective is to extract and validate buyer data, sender data, and item type information, quantity, quality, etc. To ensure the right items were invoiced, delivered, and obtained.
And the solution is a pre-trained model that aims to understand various types of invoices and billing documents for extracting data key/value pairs and the layout of a table. It reflects many differences in documents where not all the formats are understood. Contact us to get more information.
Written by: Jimmi Chandra