Achieve Accuracy in Document Processing with Configurable Confidence
Understanding unstructured documents in the real world demands extensibility and full configurability. Inability to tailor the platform to your organization’s particular needs and use cases will ultimately mean the platform will not be used to its full potential, and that means time and money is wasted.
Configurable Confidence Comes from a Layered Approach
Where in the document understanding process is configurability really needed? To be confident of your results, you must be confident at several different layers of the document understanding process, including:
- OCR confidence: Your optical character recognition technology should ensure that text characters are digitized correctly. If inaccuracies exist, changes made to a configurable platform can help you reduce the volume of inaccurate output.
- Model confidence: The deep learning model used should enable you to understand the confidence on the values that it returns.
- Logical validation: Your document understanding solution should be able to add line items and match them to a total figure.
- External validations: Your document understanding solution should also enable you to validate data points of your choice via third-party APIs. For instance, perhaps you have a field in a document that requires a state tax ID number. You should be able to configure your document understanding solution to integrate with the state’s Department of Revenue to confirm the ID number given.
- Internal validations: You should also be able to configure your document understanding solution to validate outputs based on internal data. For example, the solution should be able to confirm whether a specific customer name in a document matches a customer name in your system.
- Custom validations: You should also be able to configure your document understanding solution to take into account your own pre-determined business rules when validating data.
When your document understanding solution allows for these multiple layers of validation, the result is greater accuracy. With configurability baked in, you can feel more confident that your outputs are accurate.
Instabase was designed with configurability top of mind, so it’s user-friendly and adaptable to each client’s particular set of needs. With the Instabase Automation Platform for Unstructured Data, our customers can easily plug in models or swap them out as needed. Utilizing the most advanced deep learning models available, Instabase is able to ‘read’ client documentation more accurately, regardless of the type or format of the document.
In the sections that follow, we discuss some of the top ways configurability aids in document understanding, making it a crucial characteristic of market-leading document understanding software.
Better Document Understanding
The inherent configurability built into the Instabase Automation Platform enables customers to quickly build and deploy task-specific models, no matter how often those tasks change. Deep learning models continue to grow in accuracy over time. Applying deep learning to the process ensures verification and accuracy of document understanding. Because deep learning models are pre-trained with a large volume of datasets, they are able to continue to adapt to new tasks working with fewer samples than classical machine learning models require.
Configurable confidence comes into play as the model processes documents via a series of split-second enquiries. For example, questions the solution might ask of a document containing a specific date range in a particular format might include, “Does the extracted value for “Period Ending” follow the expected date format for this document?” And for a document bearing company letterhead, Instabase will immediately identify the text as an organization name, and it might ask, “Does this entity exist in a public-companies registry?”
Instabase’s deep learning models, when combined with structural detection, algorithm constraints, key values, and entity detection, deliver clients a superior document understanding solution.
Quicker Build and Time to Value
Deep learning makes tailored configuration of Instabase solutions easier and faster.
Many of the document types with which business professionals work on a daily basis are highly variable, complex documents, sometimes better known as unstructured data. They are not ideal for data extraction using traditional machine learning models because they lack significant natural context. But by combining DL models and other, more rigid technologies, Instabase is able to expedite the building of tailored solutions for our customers.
Deep Learning allows you to quickly annotate training data so the model can learn your unique workflows. This capability allows Instabase to eliminate the many steps involved in writing and fine-tuning hundreds of lines of code, getting working solutions into your hands that much sooner.
Configurability also ensures that your document understanding solution can easily scale as you grow. The ability to build and deploy models quickly, swapping them out as your needs and requirements change is a key differentiator of Instabase. While legacy providers rely on rules-based models that are rigid and difficult to configure to your unique needs, the Instabase approach of combining deep learning techniques with more traditional methods creates a scalable solution to document understanding that enables you to scale your business without having to add more staff to support expanded document volume.
In our solution marketplace, Instabase allows for the deployment and sharing of applications between teams. It also lets companies ‘slot in’ ready-made apps as fixes to common organizational challenges. Our developer exchange lets clients use and publish various platform functions and tools across different problems to overcome hurdles even faster.