Applications of Trapeze
Although Trapeze can be integrated with back-end systems to enable functionality such as three-way matching, it does not have to do this to achieve its high-accuracy rates. Many other capture systems require some sort of database matching to achieve acceptable recognition rates. Integrations with third-party systems to access data can be costly and time consuming, as well as open up back-end systems in a way that could compromise security.
Setting up a Trapeze IDR application typically involves running several hundred sample documents through the software, mainly to adjust the image processing components, so that the cleanest possible characters are fed to the OCR engine. Sample documents are also all that is needed to invoke Trapeze’s auto-document classification capabilities. An administrator can group specific types of document images together from the sample set, and Trapeze has the ability to learn the characteristics of each group and compare them to the characteristics of images being captured in the future.
Trapeze’s Smart Auto-Classification
In a mortgage file application, for example, a user might scan 100 files as a sample set, setting up separate groups for deeds, W-2s, credit reports, closing forms, etc. Trapeze should then know enough to automatically classify the contents of any mortgage file captured thereafter. Patient records are another application for auto-classification, especially as healthcare organizations transition to electronic medical records systems to meet government requirements.
Any unrecognized document is placed in a queue for manual classification. Trapeze learns from this process, and the next time it encounters an image with the same characteristics, it should be able to automatically classify it. These same learning capabilities are applicable in data extraction applications, for both structured and unstructured forms. Trapeze has a browser-based validation and data entry interface that enables operators to select an area on an image and use the OCR/ICR results from this selected area to automatically populate a data field. The next time Trapeze encounters a form with a similarly structured data field in the same location, it should automatically capture the data.