Rossum’s New Ebook Shows You How Automated Data Capture Can Increase Profitability

Document data capture is vital to your business operations. Accurate, timely output keeps functions across your organization running efficiently and effectively. The quality of the data you extract from documents and import into various symptoms can impact your business relationships and expenses.

Your data processing method also plays a critical role in resource allocation. Manual approaches can bog staff with repetitive, error-prone, and ultimately costly tasks; an automated approach can free experts up to engage in stimulating value-generating activities.

You can learn much more about document data capture in our exclusive new ebook, Do Not Enter Data, Capture It: How Smart Document Processing Can Improve Your Business, written in cooperation with Software Suggest. 

You’ll discover just how important this often overlooked and undervalued process is. You will also learn:

• How manual data entry and traditional optical character recognition (OCR) solutions are costing your company significant amounts of time, money, and resources
• How artificial intelligence (AI) technology can extract document data for your business processes quickly and precisely
• How you can put AI-powered data capture tech to work for you today, in just minutes

We’ve also included an appendix that introduces Rossum’s handy, easy-to-use Magic Grid feature.

Download this handy ebook today and see how Rossum’s data extraction platform can produce a 4.5x reduction of invoice processing costs. That means reducing the manual processing cost of $2.03 per invoice to $0.45 per invoice!

To show you what you can look forward to, here are a few excerpts. 

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What Is Document Data Capture?

Document data capture is the process of extracting information from documents and placing that information into any number of systems – from paper ledgers through digital spreadsheets to enterprise resource planning (ERP) platforms. It is a simple concept; in practice, the methods and tools used to carry it out determine its simplicity or complexity, cost, and impact on business processes.

There are three ways of collecting information from documents: manual data entry, template-based OCR solutions, and smart automated OCR solutions. All have their place, though advances in smart technologies and the evolution of best practices across business functions are rendering some methods obsolete. 

Document Data Capture Methods

The Future of Data Capture Systems: Imitating Human Behavior

Someone should have perfected document data capture a long time ago. At least that’s what many people think, especially those who have never tried to solve the problems that come with traditional methods.

This occurred to us as we started talking to customers in our endeavors to find the ideal application of Rossum’s machine vision technology. We were surprised to discover not only the complexity of the problems with traditional data extraction, but also how much of an advantage the human mind has over a fixed algorithm. These discoveries led to Rossum’s unique approach to data capture, enabling it to stand out in its domain.

As the people who founded Rossum, we’re your standard nerds with several major accomplishments in machine learning, computer vision, and AI between us. In 2016, we decided it was time to stop fiddling around with AlphaGo and image recognition – we wanted to focus on one extremely difficult problem that would impact the lives of millions of people every day. That problem turned out to be document data capture.

Data Capture Solutions: Manual vs. Traditional OCR vs. Cognitive

There was a time when document processing was a completely manual task. Most, if not all, companies had data entry clerks entering and updating information from paper documents into various business systems. This approach is slow, error-prone, and not scalable.

The arrival of OCR solutions held the promise of efficient, accurate, and scalable data capture with minimal human input. However, to deliver on that promise, the software requires templates and rules for every document it processes. This is an excellent solution for your business if you’re dealing with a limited number of document formats; however, it’s more likely that you’re handling a wide variety of formats. Therefore, a traditional OCR solution makes you dependent on developers to set up new rules and templates; it also comes with high implementation and maintenance costs, making traditional OCR potentially more expensive than manual data entry.

Better Data Capture for Better Business

Document data capture may seem like a minor process in the grand scheme of your enterprise’s operations. However, as demonstrated in the ebook, the accuracy and timing of its output can have a huge impact on many of your business processes. 

Fast and precise data extraction can help streamline critical functions and simplify complex workflows. An AI-powered automated solution that features an intuitive UI can minimize human intervention, freeing up manpower to handle higher-value tasks.

Before We Go: How Rossum Turned the Tables on Tables

In early 2019, we released a new version of Rossum that captures line item data when processing documents. This resulted in a platform that delivers peerless accuracy through its “Magic Grid” approach to human-computer collaboration on data capture.

We approached tabular data extraction from two perspectives – the integration available to implement a table data capture process, and the technology required for automatic table data capture. Our AI research team made a series of breakthroughs in the required automation technology, which gave Rossum the ability to pre-capture a large portion of tables automatically.

While Rossum can read tables, it still needs to enable you to validate and correct errors in the captured data. The platform should augment your role as a human operator in the automated data capture process, helping you check and fix results quickly while keeping you in control of the data capture process.