In the late 1960s, the first OCR techniques turned scanned documents into raw text. 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 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prevailing assumption is that machine learning, often embellished as “AI”, is the best way to achieve this, superseding outdated and brittle template-based techniques.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 It's no surprise that ML-based document parsing projects can take months, require tons of data up front, lead to unimpressive results, and in general be "grueling" .These issues strongly suggest that the appropriate angle of attack for structuring documents is at the data element level rather than the whole-document level. In other words, we need to extract data from tables, labels, and free text; not from a holistic “document”.
sensiblehq kevestun This tutorial shows you how to develop a Linear Regression and compare it to Random Forest and Support Vector Machines models.
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