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

<strong>Machine Learning, Explained: What Machine Learning Means for Automated Document Processing</strong>

Machine learning is making data collection easier and more accurate than ever. The introduction of machine learning processes isn’t intended to replace human-supervised learning — it’s a way to optimize output with artificial intelligence (AI) that simulates human behavior.

Document processing has been one of the most successful testing grounds for machine learning. With high volumes of invoices, receipts, applications, and other documents, manual document processing can be a spirit-draining and inefficient task.

Existing technologies like optical character recognition (OCR) can work for highly structured documents, but any degree of unpredictability can require manual intervention. By implementing algorithms that continuously improve over time, machine learning is revolutionizing document processing — across businesses, and across industries. 

What is Machine Learning? 

According to Merriam-Webster, machine learning is defined as “the process by which a computer is able to improve its own performance (as in analyzing image files) by continuously incorporating new data into an existing statistical model.” 

In more general terms, machine learning uses algorithms that adjust over time, as more data is collected. These algorithms are usually based on open-source frameworks like TensorFlow, which serve as the basis for machine learning structures. These algorithms simulate human learning by factoring in statistical analysis to make more accurate predictions. 

Within machine learning, there are several different structures of algorithms. Artificial neural networks form the basis for many machine learning services — especially cloud-based solutions for automated document processing. With a neural network, an input layer of nodes is followed by an output layer, which receives data from the input layer that passes an acceptable threshold of values.

With the input-output structure of a neural network, machine learning programs can make intelligent decisions informed by statistical analysis. Traditional (non-deep) machine learning uses three or fewer layers in its neural network.  “Deep learning” occurs when there are more than three node layers in a neural network. 

How Machine Learning Works in Modern Intelligent Document Processing (IDP) 

It’s impossible to have machine learning without artificial intelligence (AI). AI is an umbrella term that refers to machines that reproduce human behavior and intelligence — from decision-making to performing physical tasks. Within the realm of AI, machine learning simulates human intelligence by gathering data, making predictions, and improving over time based on statistical analysis.

Machine learning is highly useful for data extraction since it can make inferences about semi-structured and unstructured data using context clues. A machine learning-driven approach to document processing is intelligent document processing (IDP). Here’s how machine learning works in IDP: 

  • Get ready with pre-processing. Before IDP can begin processing data, pre-processing uses AI technology to capture and ingest the data at hand. Pre-processing includes filing documents into the right categories, and setting up the right document layout. 
  • Capture data. Data capture is the “reading” step of IDP. By using machine learning, IDP can intake visual data into its nodes, and capture semi-structured and unstructured data with increasing accuracy. 
  • Validate data. Validation is the “review” step of data processing — identifying and sorting the data using workflows, which can improve over time with machine learning. 
  • Follow up with post-processing. Post-processing is the most manual process in IDP, although it can also be highly automated. The follow-up allows you to correct any inconsistencies in the results of machine learning. Post-processing also helps to identify overfitting — an over-accurate detection of “trends” which aren’t really there. 

The Benefits of Using Machine Learning in Modern IDP Solutions 

The benefits of IDP are far-reaching, with implications for small businesses and major corporations. Compared to a traditional OCR template, IDP solutions circumvent the manual effort it takes to gather and analyze semi-structured and unstructured data. This is not only a time-saver, but it can improve employee productivity by avoiding soul-draining manual document processing. 

Even with highly structured data, the specifics of traditional OCR make it difficult to avoid close manual supervision. Because OCR templates need to match the data exactly — without typos, handwriting, or variation in formatting — even invoices with strict formats will require manual supervision. 

With the capacity to receive documents from hundreds of channels, in hundreds of formats, IDP improves accuracy while saving time and hassle. In fact, with over 98% accuracy, IDP can even process documents with accuracy that rivals humans. You can quickly scan, process, and match data without setting a single template. 

Industries that can benefit from IDP range from financial services to manufacturing, insurance, healthcare, and more. Wherever document processing is part of your business model, IDP provides an opportunity to improve accuracy and efficiency. 

Machine Learning at Rossum 

Rossum brings IDP to your business, with user-friendly machine learning. As machine learning becomes the new norm for document processing, it’s important to find a solution that is easy to use — for a seamless transition to automated processing. With Rossum’s automated IDP solution, businesses across the globe can route documents from diverse sources, in different languages and formats. In a single document processing environment, Rossum makes machine learning intuitive. 

Take the step into machine learning, and learn how Rossum can help to streamline your organization. IDP provides a competitive advantage that can quickly pay dividends to your enterprise. By simulating human learning, Rossum helps you to reduce risk — and to satisfy your customers. We offer a 14-day free trial of our cloud-based IDP. Learn more about machine learning at Rossum, and find out how Rossum can help your business grow.

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