Deep Learning, Explained: What Deep Learning Means for Automated Document Processing

Today’s new wave of tech innovation — sometimes called the Fourth Industrial Revolution — is comprised of services that use computing to automate tasks in smart and unconventional ways. With artificial intelligence (AI), businesses can improve efficiency and productivity by simulating human intelligence. 

A primary advancement in this method of computing is deep learning. Deep learning is an integral part of AI, offering a more complex approach to neural networks. 

Businesses large and small can benefit from deep learning technology, which continues to allow for human supervised learning while automating rote and repetitive tasks. Let’s take a look at how deep learning works — and what it means for document processing. 

  • What is Deep Learning?

Macmillan defines deep learning as “a type of machine learning in which neural networks learn by being exposed to vast amounts of data.” 

To understand deep learning, it’s important to first understand what a neural network is — and how it works within the realm of machine learning. 

The umbrella concept that contains all of these processes (deep learning, neural networks, and machine learning) is a core technology called artificial intelligence. AI automates processes like decision-making, approximation, and prediction that previously only humans could make. Today, AI technologies are used in services ranging from chatbots, to voice assistants, manufacturing automation, and more. 

Machine learning is a subset of AI that allows machines to learn over time, with statistical algorithms that make corrections and improvements. Much in the way humans learn, machine learning uses a neural network to identify patterns, and to make predictions.

Deep learning is a type of machine learning. In deep learning, neural networks have three or more layers (including the input layer and the output layer). This allows machines to process large amounts of information, and to perform more complex tasks using multiple nonlinear transformations. 

  • How Deep Learning Works in Modern Intelligent Document Processing (IDP)

Deep learning lies at the heart of modern information processing. Before deep learning revolutionized document processing software, optical character recognition (OCR) required preconfigured templates to identify and process perfectly structured documents. With traditional OCR, variations in format required a new template — or a manual correction. 

Intelligent document processing (IDP) uses deep learning to make predictions and approximations — which means an IDP can use its neural network to identify patterns without the use of templates. 

By making precise predictions using deep learning, IDP allows businesses to process semi-structured and unstructured data. IDP uses multiple layers of its neural network to structure data that was previously unstructured, and to organize information into meaningful categories. 

Deep learning algorithms can learn over time, which means they can extract information more accurately as they gather more data. Instead of setting templates that extract static features, deep learning allows IDP to bring information using an input layer — to process images using multiple deep learning layers — and then to express the result in an output layer. 

  • The Benefits of Using Deep Learning in Modern IDP Solutions

IDP helps to save money, while simultaneously improving the employee experience. As document processing becomes increasingly automated using deep neural networks, employees can reduce the amount of time they spend on rote data processing work — and become supervisors to the IDP process. In this Fourth Industrial Revolution, deep learning is a key strategy for making businesses competitive. 

Deep learning has revolutionized the world of intelligent document processing. IDP uses deep learning to identify patterns, and extract features that are important to the task. The end result of this process is that deep learning increases the accuracy of document processing, while also improving its efficiency. 

Complex deep learning processes, like convolutional neural networks and recurrent neural networks, are even using deep learning to analyze vision and speech.

IDP solutions use deep neural networks to quickly become even more accurate than manual processing. In less than a month, an IDP service can learn how to process information with over 98% accuracy — and avoid human error altogether. 

Dive Into Deep Learning with Rossum

At Rossum, deep learning is at the heart of IDP technology. By using deep neural networks, our cloud-based document processing can reduce time to value, with a fast deployment time and immediate improvements. As soon as you upload your first few documents with Rossum, its machine learning algorithm immediately begins to improve. 

If your business is ready to make the switch to intelligent document processing, our team at Rossum is here to help. We offer a free 14-day trial that allows you to see for yourself the benefits of deep learning in document processing. It’s a service that’s easy to deploy, easy to supervise, and brings fast results. 

For more information on how Rossum works, visit our website — and bring your organization into the future of document processing.  

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