Case Studies - Invoice Processing

Invoices are inherently complex and variable. Reconciliation of invoices is the process of extracting relevant information and storing them in a structured format to be easily tracked and managed.

Currently, organizations are heavily dependent on OCR techniques to automatically extract text out of the digital image of an invoice. While it is a step ahead of manual invoice processing, there is a fundamental reason why it is not considered a complete solution to the problem at hand.

Invoices from different sources tends to differ. There is no guarantee that critical information such as the dollar amount or invoice number will appear exactly at the same place across different documents. In other words, a typical OCR based solution does not always know where to 'look' to find the necessary information. Some organizations tackle this problem by creating a large number of static templates to address each invoice layout. This works as long as the number of layouts is small. It is also not a sustainable solution because an organization cannot keep on building new templates for every new layout.

The goal here is to build a solution that can streamline the above process by automating invoice extraction.

The complexity of the task is mainly due to the changing layouts of text, which is why a template-based OCR solution fails. Minerva unleashes the power of deep learning techniques combined with existing Optical Character Recognition technology to automate the process of extracting structured and unstructured information from invoices.

The neural networks are trained so that they learn to identify specific fields or columns that need to be extracted. This makes the solution generic enough to take care of any type of invoice.

Minerva performs better with periodical retraining of models with new data and has strict model governance in place.

  • Speeds up invoice processing activity.
  • Reduces manual entry effort and associated costs.
  • Improves invoice processing accuracy by decreasing the likelihood of incorrect entry by humans.
  • Expedites invoice turnaround time, improves vendor relationships, and strengthens brand image.
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