Many times we are asked what makes our software different from “typical text parsing” solutions for PDF data extraction. It’s a good question that reveals that there are a lot of misconceptions and confusion in the market. To illustrate, let’s start by quickly reviewing text parsing. If you have a searchable PDF document, there are plenty of tools that will allow you to perform PDF data extraction of the text from that document. From there, various parsing methods are used including simple term searching to more-complex use of regular expressions. But when dealing with text, what you don’t get is the very useful, and often critical, context of the document.
The Challenge with PDF-oriented Data
Because PDF documents are constructed in a very different manner, the ability to rely upon character-level and word-level order is very difficult. This makes PDF data extraction more difficult than other forms of document-based extraction. If you have ever tried to copy-paste text from a PDF document into Word, you have likely witnessed not only a loss of formatting, but often characters or words are left out of the sentences are not in the correct order. Because of this, typical text parsing techniques don’t provide a good solution. It becomes even more of a challenge if you need to find specific paragraphs or data that is organized as a table. This is where content-aware parsing steps-in.
Report Miner can certainly extract text. What makes it different is that it uses a lot of AI-based technologies rooted in computer vision to gather as much context as possible regarding the document. It starts with character-level analysis and moves all the way to identifying key attributes such as headings, paragraphs, reading order, and even tables. It can do this because the technology analyzes at both an atomic and holistic level in a similar way a human would.
The result is a higher-level of understanding of any given document so that the precise data you need is located.
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