The most popular and comprehensive Open Source ECM platform
Let us know more
Why Intelligent Document Processing Isn’t as Easy as It Sounds
For decades, anyone dealing with documents has dreamed of the day software could read and understand them as easily as a person. Intelligent Document Processing, or IDP, is supposed to make that dream real. The problem is, documents are messy. Even two invoices from the same supplier might look different. Add in variations in layout, font, language, and the occasional embedded object, and the road to automation starts to feel like an obstacle course.
Engineering data adds a few more hurdles. CAD files can contain hundreds of layers and references that only make sense if you know how they connect. Scanned drawings often come with faint notes in the margins, handwritten measurements, or blurry stamps from decades ago. Technical tables might be nested three levels deep, defying the logic of most parsing tools. Every document becomes its own small puzzle.
Traditional OCR and NLP tools struggle because they were built for simpler patterns. They can spot text, but not context. They might extract a number but not recognize whether it’s a dimension, a tolerance, or a part code. When the content runs across different file types or mixes image and vector data, even the best rule-based systems start to crumble. AI techniques are closing that gap, but they still require careful training, tuning, and human oversight to make sense of the complexity.
Perhaps the most common misconception is that IDP is “plug and play.” It isn’t. Successful implementations need time, domain expertise, and a feedback loop that teaches systems what to recognize and how to improve. The intelligence isn’t in the software alone—it’s in the combination of technology and the people guiding it. Getting there takes patience, but for organizations that handle engineering content daily, it’s the only way to move from reading documents to truly understanding them.
