Neural Networks, Explained: What They Mean for Automated Document Processing

Neural networks, also known as simulated neural networks (SNNs) or artificial neural networks (ANNs), fall under the umbrella of artificial intelligence (AI). These networks are similar to the human brain, both in their structure and ability to learn, recognize patterns, and solve problems.

The power of neural networks has allowed for more advanced software programs that cut costs and save time. Everything from advanced image recognition to self-driving cars has become a reality thanks to neural networks.

These advanced networks are also changing automated document processing for the better. Instead of being slow and inaccurate or requiring lots of manual oversight, modern intelligent document processing (IDP) solutions that rely on neural networks are smart, fast, accurate, and highly automated. This puts IDP leagues ahead of traditional optical character recognition (OCR) and other template-based document processing solutions.

Organizations of all sizes and industries can benefit from neural networks and IDPs powered by neural networks. Read on to learn what precisely neural networks are, how they are changing the intelligent document processing game, and the benefits of using a modern IDP that relies on neural networks.

What Are Neural Networks?

A neural network is “a computer architecture in which a number of processors are interconnected in a manner suggestive of the connections between neurons in a human brain and which is able to learn by a process of trial and error.”

Essentially, neural networks are a subset of machine learning. They take their name and structure from the human brain, so just as our neurons send signals to one another, the nodes (or artificial neurons) in a neural network connect and send data to each other. 

In a neural network, you can expect to find an input node layer, one (or several) hidden node layers, and an output node layer. If a node’s output is above a specific value, the node will activate and send data to the next layer of the network.

The result is a complex, intelligent system that can quickly process and learn from large amounts of data and complete complex tasks. Neural networks will become increasingly accurate over time if they are exposed to training data, such as Google’s search index algorithms, for example. 

Neural networks are closely connected to deep learning. In fact, some use the terms interchangeably. Essentially, any neural networks with more than three layers fall under deep learning, while those with just two layers are considered basic neural networks.

There are several types of neural networks, each with its own purpose. The most common types of neural networks include:

  • The Perceptron: The oldest, simplest neural network consisting of a single neuron
  • Feedforward Neural Networks: More complex networks with an input layer, a hidden layer (or several), and an output layer are used for everything from natural language processing to computer vision
  • Convolutional neural networks: Networks that use linear algebra principles to identify patterns in images
  • Recurrent neural networks: Networks with feedback loops that are usually used when making predictions based on time-series data

How Neural Networks Work in Modern Intelligent Document Processing (IDP)

Without neural networks, document processing software would be unable to understand data in documents that don’t follow a specific template. We would be stuck with template-based OCR solutions, which would mean spending countless hours configuring templates, reformatting documents to fit existing templates, or manually correcting data.

Neural networks, however, make it possible for IDP solutions to accurately identify patterns, even without templates. 

Thanks to neural networks, modern IDP solutions can process semi-structured and unstructured documents and data with no problem. The input node layer brings the data from your documents into the system. Then the deep, hidden layers process that information before the output layer produces the final results.

Within an IDP, computer vision, natural language processing, neural networks, and more will work together to read, understand, analyze, and process your documents. It’s a complex process, but it will seem simple and fast from your end. 

Because neural networks enable IDPs to learn and continuously improve, processing will only become faster and more accurate over time. Your IDP will be able to automatically handle invoices, paychecks, and all sorts of other unstructured documents thanks to neural networks and other artificial intelligence technologies, saving you valuable time, energy, and money.

The Benefits of Using Neural Networks in Modern IDP Solutions

Neural networks enable modern IDP solutions to become even more powerful and automated than ever. After all, neural networks help IDP solutions to understand and act like a human might. 

Rather than forcing your employees to manually enter data and, by extension, lowering employee morale and retention rates, you and your employees can step back and let your neural network-driven IDP handle most of the data processing work.

You’ll not only save when it comes to manual labor, but you’ll also save time and money when compared to traditional OCR, template-based IDP, and manual document processing. An IDP will handle those tedious, repetitive yet necessary tasks, and you can concentrate on more business-critical tasks. 

Modern IDPs can reach over 98% accuracy with the help of neural networks and other forms of artificial intelligence, leading to few or no mistakes. You can say goodbye to the hassle of fixing human errors and manually correcting OCR or outdated IDP solutions’ mistakes. 

Instead, you can put that time and money towards more essential activities and become even more competitive.

Leveling Up Automated Document Processing Via Neural Networks With Rossum

At Rossum, we know just how powerful neural networks are. That’s why our platform leverages neural networks to power advanced, automated document processing. Not only is our solution fast, but it’s also incredibly accurate and easy to use. 

Rossum can transform the way you process documents, and you’ll see a return on your investment in no time. Plus, the use of neural networks in our IDP means that it can understand unstructured documents and will continue to learn.
Ready for a streamlined document processing workflow and increased accuracy? Check out our 14-day free trial to experience first-hand how neural networks have changed document processing. In the meantime, you can also read this article to better understand neural networks.

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