Using Artificial Intelligence to simplify document capture
Processing document data quickly and accurately is crucial for any business to remain competitive in today’s fast-evolving digital age. With documents making up the heart of business processes in any given organisation — regardless of the industry or size — that organisation’s success relies on its ability to locate, access, and understand document data.
Doing this effectively requires document automation capabilities, but not all automation technologies are created equal. To enhance business processes, technologies such as advanced data capture and Machine Learning need to integrate with business applications, devices, and workflows seamlessly. Advanced document capture solutions blend cutting-edge innovations in data capture, Machine Learning, and information management into tools and technologies that improve any digital solution, system, or device performance.
This blog explores how AI and Machine Learning can simplify and enhance document capture to bring even more value to your business.
NEURAL NETWORKS SIMPLIFIED: A READY-MADE SOLUTION
Today, most Machine Learning models are inspired by how neurons in the brain need to connect and adapt. These neural network models can be applied to several processes, from character recognition to document processing. Machine Learning and other advanced automation techniques are now being applied to capture, classify, and manage information in more efficient ways, that can reduce the need for human input.
System integrators and developers can easily extend the scope and value of solutions and platforms with powerful tools to build business intelligence and boost process performance. But beyond the technology, additional factors are required for the solution to be truly successful. While some might think of Machine Learning as some kind of magic, one-size-fits-all solution, in reality, it’s an aggregation of hundreds of different algorithms and techniques. Choosing the right technique and algorithm for any given use case requires extensive specialisation and knowledge. How you represent the inputs to a Machine Learning algorithm can have a massive effect on performance, speed, accuracy, and the solution’s robustness.
Before choosing a solution, you must evaluate details like ease of implementation, level of integration with your existing business processes, and the analytics tools that go along — all factors that contribute enormously to the product’s ease of use. Tools that are ready to use and easily embed into solutions, devices, and workflows, will ensure you have access to practical yet innovative technology that works, is reliable, and scalable over time.
DYNAMIC DOCUMENT CLASSIFICATION: LOW MAINTENANCE, HIGH PERFORMANCE
Traditional recognition and capture solutions often rely on predefined business rules to process information. These rules place parameters around information capture to increase data recognition accuracy and reduce manual entry over time. However, these rules must be comprehensive to be effective and must include variations in documents for complete coverage.
Advanced Machine Learning capabilities make it straightforward to automatically categorize scanned or digital documents based on their content, even where the content is highly variable. Documents are automatically categorized using learn-by-example methods that can continue learning dynamically, adapting to new document types, and even learning from users’ actions. This type of automate-on-the-fly capability is light years beyond the old-school scan-and-store approach to document management. Gone are the days of manually defining parameters to enable your document classification processes.
HYBRID TECHNIQUES: WHY MACHINE LEARNING ALONE IS SOMETIMES NOT ENOUGH
But Machine Learning is useless without the data samples to train the system–and in the case of techniques like deep learning, it requires a lot of data. Obtaining these document samples should not become a burden or create privacy issues for your company. While some cloud providers may offer data extraction based purely on Machine Learning, these systems are “data-hungry.” They may hold any documents that your users are processing to further train and improve the system.
For anyone with sensitive documents–aka everyone–that’s simply unacceptable. New technology must be honed to ensure that the number of training documents required for effective machine learning is realistic and minimal. Your users have full control over and exclusive access to any documents used for training.
Additional options such as rules-based and NLP (natural language processing) techniques can be used alongside Machine Learning if and when they’re a better fit for the job. A typical example of this is in the area of data extraction where the solution can offer practical hybrid techniques to allow for additional information or metadata to enter the classification process and improve performance on challenging documents without sacrificing any learning power.
THE DOCUMENT AUTOMATION TOOLBOX
Depending on your specific use case for document capture, document automation technologies can be mixed and matched to meet your particular business needs. From high-performance OCR (Optical Character Recognition) to advanced document automation capabilities, modern solutions provide modular, ready-to-use technologies that you can plug into your systems for optimum deployment speed without compromising your existing offerings. The modules can be easily reconfigured across multiple projects so that you can make the most of your advanced data capture solution and bring more value to your customers.
Whether it’s a simple reference number or complex data with a highly variable location, AI makes it easy to reliably extract data from any document, even where the content is highly variable in format and layout. The solution is complete with a vast library of extraction modules that are ready to use, with general-purpose modules for common data like dates and reference numbers, modules for extracting personal information, and modules for invoice data.
A SCALABLE SOLUTION FOR EXTENDED SCOPE AND VALUE
Not everyone is a machine learning guru, and that’s ok. AI-driven tools can simplify and accelerate document automation integration into your existing software solutions, document processing services, and workflows. The turnkey, modular automation techniques provide your developers, integrators, and providers the ability to extend their business solutions’ scope and scale and extract more value from existing platforms to help boost your organisation’s overall performance.
Since 2012 our FileHound Digital Transformation Platform has been fully designed, developed and enhanced by the Element3 Technology product team. FileHounds integrated AI powered SmartCapture technology delivers structured data from unstructured documents, increasing the efficiency of a wide variety of systems and processes.
FileHound offers an innovative blend of machine learning and automation technologies that capture, classify and manage information in new and more profitable ways. All delivered in the cloud, no need for additional 3rd party capture products – this feature in engineered into FileHound.
ABOUT THE AUTHOR
George Harpur is co-founder and CEO at Aluma an Element3 & FileHound technology partner. Since earning his PhD from Cambridge University in the mid-90s, George has been applying these skills to document automation and has played a prominent role in the development of multiple industry-leading products.