Continuous Learning: How it Improves Data Accuracy in Document Data Extraction

One of the key metrics used to evaluate the utility of document automation is accuracy. It’s a simple way of describing how much information pulled from a document by the platform is correct. Depending on the software you use, as well as the complexity of your document and/or the process it’s used for, accuracy may be high or low.

However, the most modern platforms include the feature of continuous machine learning. In this article, we’ll explain how it impacts model accuracy and why you should opt for a tool that gives you this unique feature to get the most out of your automation.

For every type of document automation, the end game is to establish a workflow where the role of a person is to supervise the process and analyze the results – rather than perform simple and repetitive tasks manually. That’s the job of the AI.

Consider one of the examples we’ve seen as a use case of Alphamoon. A company that helps businesses deal with trademark registrations needed software for reading invoices.

It was a time-consuming process, filled with tiny errors that were hard to find immediately but “liked” to emerge months later, causing quite a headache. Alphamoon helped by automating the process of downloading invoices from the mail inbox and then reading data that was used for cost calculations.

That way, the number of errors was vastly reduced, and the team no longer wasted time completing a daunting task.

Even when accuracy is close to 100% – meaning that all information was correctly read and taken from the text or tables in the document – there’s always a chance of an error.

Accuracy also depends on the type of documents. Some are tougher to work with, and the platform makes mistakes when reading information. The more complex the table or the more structured the document is, the harder it might be for the AI algorithm to work well.

That’s where continuous machine learning comes in.

What is Continual (Continous) Learning?

The simplest definition applied to document automation is as follows:

Continual learning is a method of improving an AI model thanks to user feedback. This method allows users – such as you – to correct a mistake made by the platform. Correcting mistakes made by the AI algorithm increases future model accuracy when working on similar documents.

Intelligent document processing revolves around the constant improvement of results yielded by the AI algorithm, which decreases the need for human intervention. The platform aims to act as an intelligent assistant to the business user – one that works in the background and requires minimal supervision.’ – explains Adam Gonczarek, CTO at Alphamoon.

Applying continual learning yields several benefits for business users, and we’ll discuss them in the next section.

The Advantages of Continuous Learning

In the real world, dealing with documents can be quite messy. When documents come in, they can look different from one another, and even among similar documents, there can be variations.

That’s life – nothing’s pitch-perfect and certainly not administrative work.

So here’s how the world actually works:

  • Manual tasks are time-consuming, and to save that time, people can do things hastily. That leads to incomplete and chaotic databases created with data taken from documents. Subsequently, the lower quality of such a database impacts every process and decision that uses this information set;
  • Low visual quality, lack of order of information, and over-complexity are just some of the issues that characterize work with documentation. All of them are challenges for document reading software;
  • There are no universal rules set for administrative work; they differ based on geography, industry, and even company-specific characteristics;
  • Over time, the types of documents we handle can change, or new ones may start arriving;
  • When creating a filing system, sometimes we start with a few categories, like, let’s say, “orders” and “claims.” But later, we might want to add more categories, like “cheques.” The model needs to be adjusted;

The above is just a fraction of all the challenges.

So, it’s important to be aware that things can be unpredictable in the real world of business. That calls for solutions that prioritize flexibility of use and quickness of adjustment.

Considering the above, you may already see why continuous learning is a crucial feature you should be aware of when choosing a platform for document automation.

Continuous learning helps you:

  • Increase the model accuracy, which eventually saves you time (fewer mistakes means more documents that can go through the platform without your intervention)
  • Adapt the model to your specific documents, fine-tuning it to your needs
  • Expand and scale your process without the need to switch to different platforms
  • Solve issues such as bad quality or messy templates, because the AI gets better with every correction you make
  • Decrease operational costs thanks to the more effective tool

Now that the benefits have been explained, let’s take a closer look at how Alphamoon’s approach to continuous improvement of AI models empowers you from “document one” – instead of having to retrain the model with an initial batch of documents.

How Does Continual Learning Work in Practice

Okay, now let’s drop the tech discussion for a minute, and think about the concept of instant gratification. Research indicates that more and more employees see no value in annual performance reviews but prefer one-on-ones to hear how they’re doing.

The same principle applies to bringing new tools to your daily work. If you invest time in configuring or working with a tool without seeing direct, short-term results, you might feel less convinced to use it long-term. Modern businesses need to be agile, and flexible.

We no longer want to wait to see how things turn out.

And now we’re back to the matter at hand. What if your entire model improved from the very first document you added and corrected? Alphamoon’s approach to continuous learning is built with that in mind.

The algorithm that constitutes the foundation of the platform solves smaller automation tasks and, therefore, arrives at significantly better results.

continual learning technology

That principle, used in machine learning models, means that they also achieve lifelong learning – a continual improvement over their operational lifetime.

All of it happens in the background and requires no coding skills. It’s easy – start by clicking below and creating your account.

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When you sign up, you will first need to create your process. After fine-tuning the model, go to “Configuration.”

continual learning dashboard

Clicking on the button will bring you to the dashboard, where you can change the fields that the model is supposed to read and get from your documents, as well as turn on continuous learning. Go to the “Continuous learning” section.

continual learning configuration

After turning it on, you can decide whether the model will improve automatically with each correction you make or if you wish to train it on your own by clicking the “Train now” button. This would be periodic retraining, which is recommended when the time frame between model updates is important to you.

There’s also the table containing your models, which helps you track updates.

CL trening history


All things considered, continuous training in the AI model should be an essential part of your document-reading software. If you are looking for a means of saving time and lowering the number of mistakes related to data reading from documents, that’s your way.

Alphamoon’s approach to continually improving AI models is unique and brings you instant enhancement. You can test our platform for free to see if that’s your cup of tea.

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