Equidox by Onix, the premier PDF remediation software developer, has added smart detection tools to its robust PDF remediation software. Using these new artificial intelligence-powered tools to identify page elements, complex content is now faster and easier to tag during remediation.
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Pat Needles, Executive Director of Sales, says, “Remediation to achieve compliance will continue to be needed as long as organizations are using PDFs. Our goal is to make that remediation as fast and easy as possible. Human intervention will always be needed, but Equidox continues to add automation to our product – we want to streamline the process to save organizations time and money.”
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The artificial intelligence-powered Table Detector detects table rows and columns using a proprietary machine learning model. According to Equidox’s data scientist, David Freelan, “The new detector uses ten new settings: five for columns and five for rows. Each uses a different detection functionality to place row and column delineators in order to tag the individual cells of the table. These work independently, so are useful for a wide variety of table formats. Some detect existing lines, some detect text, some detect spacing. By choosing one setting from the options for rows, and one setting from the options for columns, the machine learning detection process can more accurately pinpoint how to tag the content into cells.” A table summary is automatically populated with the number of headings, merged cells, spanned rows, and columns. Finally, an HTML preview shows the completed table so users can verify they’ve correctly tagged various elements of the table.
The new smart List Detector is just as easy, even for complex nested lists. Create and label a zone as a list, then adjust the List Detector to one of five unique settings until all list items are identified and nested correctly. The List Detector automatically detects all items: no need to interact with complicated tag trees involving layers of list items and list bodies. The HTML preview screen shows the list correctly tagged to ensure accuracy.
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