Sensible

Machine Learning is the Wrong Way to Extract Data From Most Documents

Josh Lewis
Tuesday, July 26, 2022

Documents remain underused in software toolchains, and valuable data languish in PDFs. The challenge has shifted from identifying text in documents to turning them into structured data suitable for direct consumption by software-based workflows or direct storage into a system of record. The best way to turn the vast majority of documents into structured data is to use a next generation of powerful, flexible templates that find data in a document much as a person would.

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Documents have spent decades stubbornly guarding their contents against software. In the late 1960s, the first OCR (optical character recognition) techniques turned scanned documents into raw text. By indexing and searching the text from these digitized documents, software sped up formerly laborious legal discovery and research projects.

Today, Google, Microsoft, and Amazon provide high-quality OCR as part of their cloud services offerings. But documents remain underused in software toolchains, and valuable data languish in trillions of PDFs. The challenge has shifted from identifying text in documents to turning them into structured data suitable for direct consumption by software-based workflows or direct storage into a system of record.

The prevailing assumption is that machine learning, often embellished as “AI”, is the best way to achieve this, superseding outdated and brittle template-based techniques. This assumption is misguided. The best way to turn the vast majority of documents into structured data is to use ....

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