Optical character recognition (OCR) turns an image into characters: it reads what is printed or written and outputs text, roughly in the layout it found. AI document extraction turns a document into fields: it reads the page, works out which number is the net weight, the invoice total or the contract number, and returns structured data. Many AI extraction tools read OCR text as well as the image, so the two are less rivals than layers. What decides the quality of the output is what the model is given to read, and whether its confidence scores can be trusted to send the right documents to a person.
What the usual answer says
The usual explanation presents AI extraction as OCR's successor. OCR relies on templates and rules and breaks on new layouts. AI understands context, handles any format and is more accurate. That is broadly right about capability. It leaves out how either one fails, and how you would know.
Both make errors that pass through
A 2024 benchmark, OHRBench, tested "OCR solutions" on 8,561 document images with 8,498 question and answer pairs. Those included traditional OCR pipelines and vision-language models used for OCR. Its verdict was that "none is competent for constructing high-quality knowledge bases" for AI retrieval systems. It measured retrieval quality rather than field extraction. Still, the point carries: neither kind of reader is clean enough to skip checking. Its authors separate formatting noise from semantic noise, meaning wrong characters, and a wrong character in a weight can look entirely plausible from either tool.
AI adds a further kind of error. Zhentao He and colleagues, in a June 2025 study of identity cards and invoices with simulated degradation, found models often fail "to adequately perceive visual degradation and ambiguity, leading to overreliance on linguistic priors". That, they write, "frequently results in the generation of hallucinatory content, especially when a precise answer is not feasible". OCR garbles what is on the page. A model can also supply what isn't.
Confidence scores decide what a person sees
Most extraction tools attach a confidence score to each field and send low-confidence fields to a person. That only works if the scores mean something. Priyashree Roy and colleagues, in ConfBench, published in August 2026, tested this across 1,346 degraded document variants and more than 70,000 field-level evaluations, with three kinds of input. They found calibration "varies widely across models, from near-perfect to severely overconfident". And of the inputs tested, "OCR+Image modality results in more accurate confidence estimates".
So the practical answer to OCR or AI is often both: OCR text alongside the image makes the model better at knowing when it is unsure. A sound setup keeps three things for every document, the image, the OCR text and the extracted fields, so a reviewer can see what the model saw.
Test the confidence, not just the accuracy
A vendor's accuracy figure says how often the tool is right. It doesn't say whether the tool knows when it is wrong, which decides how many errors reach your ledger. We suggest measuring both on your own documents. Take a hundred, key the fields by hand, and run them through the tool. Group the fields by the tool's confidence and check the share that is correct in each group. If fields scored above 90% are right about nine times in ten, the scores can route work. If they are right far less often, the threshold has to move, or every document needs a check.
Timber paperwork also carries internal arithmetic, such as net equal to gross less tare, which catches some errors whatever the confidence. Our piece on handwritten trip tickets covers those checks.
Choosing for a given document
In our view, volume and variety decide most cases. A mill receiving thousands of tickets a month on one form can do well with OCR and a template, checked by arithmetic. A forest manager receiving settlements, invoices and contracts from dozens of buyers and contractors, each with its own layout, gets more from AI extraction, because no one will maintain dozens of templates. Handwritten documents need AI reading, with the most checking.
When it doesn't apply
Digital PDFs generated by software contain their text already, so neither OCR nor image reading is needed, only extraction of fields from text. And some documents carry nothing to check against, such as a contract's special conditions, which need a person to read them.
Quarri for forest management is built around how a forest operation runs, from the cruise to the settled account.
Sources
- Zhang et al., "OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation", arXiv 2412.02592, first posted 3 December 2024, v4 read (30 August 2025): arxiv.org
- He, Zhang, Wu, Chen et al., "Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models", arXiv 2506.20168, 25 June 2025: arxiv.org
- Roy, Martin, Rostami, Romo et al., "Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction", arXiv 2608.01792, 3 August 2026: arxiv.org
Quarri is an AI-native data platform for the timber supply chain. It connects buying, production, sales and inventory for forest management, sawmill, wood products and pulp, paper and packaging operators.