Artifical Intelligence in Procure to Pay

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Our team used optical character recognition (OCR) to extract information from scanned invoices using Artificial Intelligence.

What is OCR?

Optical Character Recognition/Reader is a methodology to transform handwritten or printed text into a digital format.

OCR involves deep research in Artificial Intelligence, Pattern Recognition and Computer Vision. It takes images of text as input and converts it into machine readable text.

AI has transformed the way OCR works. Earlier after the print image was converted into  text, the only way to check for errors was manually. Now using AI, an algorithm or program can be used to review and correct most errors in the document.

Overview

For our client, we were able to extract and transfer information from scanned invoices, into a digital form with matching fields.

Problem Statement


Our client was getting invoices from collaborators and vendors in different formats, and with no standard terminology and lingo. It was hard for them to convert  physical copies of their invoices into a digital form. The entire process was manual – from scanning to entering the individual data into tables. It was also fraught with mistakes. Fetching and tallying the data was also difficult.

Challenges:

The client had to manually convert printed and scanned invoices into a digital form with text fields, so that the client had the records saved on their servers.

There was a high risk of mistakes during digitisation, due to human error.

It was a time-consuming effort to manage billing invoice details using hard copies.

It was challenging for any user to capture (fetch & tally) all the billing invoice data manually.

It was difficult for users to maintain billing invoice voucher data in a structured format. Data had to be manually entered in a document and then transferred into a database management system.

Process

AI and deep learning have advanced significantly at reading text and extracting structured and unstructured information from images. We used a third-party AI, along with an OCR tool to automate the digitisation of invoices. We merged the two (AI and OCR) in an algorithm, and then coded the solution. This eliminates manual data entry, enables better logging and storage in databases, and significantly lowers errors and response times.

Solution:

We designed and developed an automated system to extract accurate data from an image scan (file format: jpg, png, pdf, jpeg) of the billing invoice.

Using a third-party AI based Text Extraction API, we designed the system to fetch correct billing invoice data.

We created a system enabling admin and other users to fetch relevant structured data of the extracted billing invoices. We tested it successfully for safety and reliability.

The system converted and saved all the billing invoice data directly into the client's database management systems.

The client can now access billing invoice files and data from the system securely and quickly.

Voucher_9_web_highlight
Physical Invoice
OCR_AI_Final_highlighted
Extracted Data

Conclusion

Our team of developers designed and developed a system using third-party text extraction API to fetch billing invoice data from scanned documents into a digital form. We stored it in a structured manner in databases, so the user can fetch the data quickly and easily. We ensured that the system was intuitive and easy for the user to handle. This tool has helped the  client in the following ways:

  1. Process automation (less manpower required)
  2. Minimise errors
  3. Reduce time and cost
  4. Easier and better storage of data
  5. Improve customer service and increase customer satisfaction
  6. Reduce carbon footprint, by ensuring fewer duplicate invoices

About DigitalFlake

DigitalFlake is a digital transformation company founded by engineers to help businesses benefit from emerging technologies. The company has worked with startups and established firms across various verticals, helping them build enterprise-scale apps to drive operational efficiency and meet business objectives

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