Invoice OCR
Document invoices — PDFs, scans and images — have to become structured data before any accounts-payable process can act on them. Drelo.ai captures those documents, extracts header and line-item data, validates what was extracted, and hands a structured invoice record to your ERP, with anything uncertain routed to a person instead of posted quietly.
Document invoice capture is the step that turns an unstructured file into data your systems can use. It covers taking the document in, working out what kind of document it is, reading its content, and deciding which value belongs to which invoice field.
In an enterprise, this is harder than it sounds for one reason: variety. Suppliers do not agree on layout, wording, language, where totals sit or how line items are tabulated — and they change their layouts without telling anyone. Capture has to cope with that variety rather than assume it away.
Pages are separated, oriented and assessed for readability.
Arithmetic, identity, completeness, duplicates and references are checked.
One normalised invoice record with the source document linked.
Where a field cannot be read with confidence, the invoice becomes an exception instead of continuing with a guessed value.
The distinction that matters most is whether a file carries a text layer. A digitally generated PDF already contains its characters, so reading it is reliable and the work is interpretation. An image-based PDF or scan carries only pixels, so recognition has to happen first and the quality of the original sets a ceiling on what follows.
Text is already present and readable. The challenge is structural: identifying which text is which field, and reading tables correctly across page breaks.
Text must be recognised from pixels first. Resolution, contrast, skew, stamps and marks all affect what can be read with confidence.
Both paths converge on the same structured output, so downstream validation and ERP handoff behave identically. The difference shows up as how often review is needed.
Extraction assigns recognised content to invoice fields. It works at two levels, and the second is consistently the harder one.
Each extracted field carries a confidence signal. That signal is what makes the difference between a system that hides mistakes and one that surfaces them — it decides which invoices continue automatically and which stop for a human look.
Fictional example data shown to illustrate the field structure produced by capture. Each field carries a confidence signal; a value below the threshold becomes an exception rather than a posting.
Extraction alone is not trustworthy, whatever technology performs it. Validation is where extracted data is tested rather than believed.
The combination matters more than any single check. A misread digit that survives recognition is usually caught because the totals no longer add up — which is exactly the point of validating rather than trusting.
The result of capture is one structured invoice record per document: a normalised set of header and line fields, in consistent units, currencies and code values, with the source document linked to it.
Normalisation is what makes downstream steps possible. Once every invoice — from a digital PDF, a scan or a structured EDI message — has the same internal shape, validation, matching and ERP integration can be defined once instead of per channel.
An exception means the process refused to pass on something it could not stand behind. That is the desired behaviour, not a shortfall, and it is the safeguard that allows automation to be used on financial data at all.
Each exception records what failed and on which invoice, and the invoice waits visibly until it is resolved. Review outcomes are what make the process improve over time.
Once an invoice record is validated, it is handed to the target system through the integration path agreed for that landscape. Integration architecture depends on the target environment and implementation requirements — the options are described on the integrations page, and SAP landscapes specifically on the SAP AP automation page.
Where an invoice references a purchase order, capture output is also what makes three-way matching possible: matching can only compare quantities and prices that were captured reliably in the first place.
Rather than compare published accuracy figures, measure on your own documents. A short evaluation answers the questions that actually affect your workload.
We do not publish an accuracy figure, because a number measured on one document mix says little about another. Accuracy depends on source quality, layout variety, language, whether line items are needed and how strict your validation is. The honest approach is to run a sample of your own invoices and measure the result on your documents.
Yes. Any capture technology reading a document can misread a field, and we do not present it as perfect. That is precisely why validation and confidence-based review exist: the goal is that a misread field is caught as an exception rather than posted silently.
OCR turns pixels into text. Invoice capture is the wider job: deciding which text is the invoice number, which block is the supplier, which rows are line items, then normalising and validating what was found. OCR is one step inside capture.
Avoiding per-supplier templates is the intent. Template-based approaches break whenever a supplier changes layout, which is unmanageable across a long tail of vendors. Layout-independent interpretation plus validation handles variety better.
Low-resolution scans, skewed pages, stamps over text and handwriting all reduce what can be read reliably. Where quality prevents confident capture, the invoice is raised as an exception rather than guessed at.
Both are in scope. Line-item extraction is harder than header extraction because table structures vary, so it is the area where confidence thresholds and review matter most — especially where line-level matching is required.
Send a representative sample of your document mix — best and worst cases — and we will discuss what capture looks like on your documents rather than on a benchmark.