What Document AI is: a complete guide to what the term actually covers

Document AI is an umbrella term for the set of technologies that read a document and work out the structure and meaning inside it, covering everything from character recognition through structuring to field extraction.

One technology never solved document work

One technology was never enough for document work.

The technology for handling documents went by the name of character recognition for a long time, because extracting characters from an image was all of it. Attach that to a real process, though, and characters alone achieve nothing.

A value acquires meaning once the table's structure is known; what to extract is settled once the form is identified; entry into a system becomes possible once fields and values are bound together. These technologies carry different names but sit on one continuum. Document AI is the expression that arose to name that continuum. What matters is that it designates a category rather than a particular product or method.

What Document AI actually is: how it differs from AI Worker

Document AI and AI Worker across three axes

First, the scope differs. Document AI covers the stages up to reading and understanding a document. AI Worker covers the execution stage of judging on that result and registering it into a system. The first names a bundle of technologies; the second names a unit of work.

Second, the objective differs. Document AI aims to turn documents into data a machine can handle. AI Worker aims to complete the work with that data.

Third, the metrics differ. Document AI is judged on recognition accuracy, structure restoration accuracy, and field extraction accuracy. AI Worker is judged on the share handled without human intervention and handling time per case. Strong figures on the first set with no improvement in the second happens frequently.

The two are not in opposition. Document AI is the ground AI Worker stands on. But mixing the two expressions in an adoption discussion sets expectations that the result will not meet, so settle first how far the handover goes.

The four technology areas inside the category

Four areas that make up the category

First, character recognition. Finding characters in an image and turning them into text, including correction of document condition and handling of handwriting.

Second, structure analysis. Identifying components such as headings, body text, tables, and figures, and establishing reading order and hierarchy.

Third, field extraction. Selecting the values the business needs and emitting them paired with their fields, with validation rules attached.

Fourth, document identification. Determining which form an arriving file is and splitting it into documents where needed.

These four do not run strictly in sequence; they interlock. Identification settles what to extract, structure gives values their meaning, and accurate recognition is what lets the rest hold.

How to evaluate Document AI

Confirm how far the product actually reaches

Products presented under the Document AI banner cover very different ranges. Products offering only character recognition, products going as far as structuring, and products including field extraction and validation all carry the same name.

Confirm point by point which of the four areas a product provides. Choosing a narrow one means filling the remaining areas with another product or in-house work, and that seam becomes a new burden.

Align the vocabulary in the adoption discussion

When the category term and the unit of work get mixed together internally, expectations and results diverge. Settle at the outset whether you are asking for reading the document or for completing the work.

If reading is the requirement, recognition and structuring accuracy are the criteria. If completing the work is the requirement, the share handled without human intervention is the criterion. Different criteria lead to different products.

Document AI in the Korean environment

Korean business documents carry specific conditions. Korean word processor files are widely used, government forms contain multi-level tables and circled line numbers, and fax intake persists.

Ask for figures measured on domestic documents for each of the four technology areas. A product that scores well on an overseas public benchmark does not necessarily reproduce that performance on Korean documents.

Frequently asked questions