How Machine Learning Empowers Data-Rich Document Management
Document management can be as complex as it is critical to a company’s success. Fortunately, advancements in machine learning (ML) technology are empowering businesses to enhance their document management and extract more data insights with less manual work.
Machine Learning’s Role in Document Management
Machine learning (ML) has shown great promise for document management since its earliest days. In fact, the first example of modern ML, which came onto the scene over 60 years ago, was a machine designed to recognize letters of the alphabet. This technology has come a long way since its origins, but today, recognizing letters and words is still an important part of how ML is applied to document management.
As with other applications of ML, document management using ML requires initial direction from humans. The “learning” aspect of “machine learning” means the system will run with this guidance and learn as it goes to continuously refine the process.
The focus of ML is on software. ML often joins forces with robotics to put computer programming into action through high-tech hardware. You may also encounter the term “robotic process automation” (RPA) to describe the way software and hardware can work together to automate tasks traditionally completed by humans.
ML can help intelligently automate a number of document management processes, including:
- Scanning documents: ML technology, combined with robotics, can simplify the process of grouping files together, removing staples or other binders, and feeding documents into a scanner. While humans still play a role in this process, ML greatly enhances efficiency.
- Classifying documents: ML models can determine what type of document something is by recognizing defining features. For example, the system may recognize that a document is a receipt, a certificate of insurance, a bill, or some other type of record.
- Logically splitting documents: For document splitting, a human first splits up a document according to their preferences, and the system learns from this information so it can replicate the same preferences with the next volume of papers.
- Extracting data: Ripcord’s ML system can also extract data from paper documents so the scanned file is far more than just a digital image — it’s a searchable document with data ready to feed straight into a company’s enterprise software systems.
Machine Learning Technologies at Work in Document Management Tools
Machine learning is a broad category that includes a number of technologies that are used for data-rich document management. Let’s look at a few prominent examples of ML technologies used in document management today.
Computer vision (CV) is the technology that allows computerized systems to “understand” visual content. Though computers can’t understand in the same way people can, they can identify and analyze information from images using CV.
In document management, CV enables a digital system to recognize what a scanned document contains and correctly categorize data from it. For example, the system might recognize it’s looking at a personnel file and save the professional photo at the top of the document, adding it to a database of employee photos, labeled with the correct name.
Optical Character Recognition
Optical character recognition (OCR) is the technology that enables computers to process text in any format. OCR technology has progressed significantly over the years, with the most advanced programs today achieving 97-99% accuracy. Ripcord’s system leverages the very best in machine learning to achieve 99.95% accuracy with data extraction.
Recognizing text is essential for document management tools to digitize — not just scan — documents. If the computer recognizes what’s contained in a document, you can search your document database with a keyword and quickly find the document you need.
Natural Language Processing
Natural language processing (NLP) is another example of a machine learning technology empowering modern document management. NLP is focused on understanding the structure and meaning of text similar to how humans understand text. OCR can capture and extract text, but NLP can provide context and recognize broader trends.
NLP is extremely useful in document management for delivering insights from unstructured data. For example, companies can use NLP technology to automatically analyze customer service call transcripts and make determinations about customer sentiment based on what they said.
How Machine Learning and AI Empower Business Intelligence
Accurate and well-organized data derived through document management tools empowers business intelligence and efficiency.
Using technologies like CV, OCR, and NLP, companies can automate previously manual processes. For example, rather than manually matching purchase orders, packing slips, and invoices, companies could use a fully automated process to intelligently manage three-way matching tasks.
Businesses can derive all sorts of valuable insights when they turn their data into something usable rather than letting it sit in unstructured digital documents or in paper files. Machine learning has a variety of valuable applications, and within document management, the benefits are extensive.