Invoice Field Extractor

How do I extract the fields from an invoice PDF or image for free without uploading it?

Open AnyTool’s Invoice Field Extractor, upload an invoice (PDF or image), and click Extract. It gets the text entirely in your browser — for a digital PDF it reads the embedded text layer with pdfjs, and for a scanned page or an image it runs tesseract.js OCR — then parses that text with rule-based heuristics and regular expressions to pull out the invoice number, invoice date, due date, PO number, vendor and address, bill-to, currency, subtotal, tax/VAT/GST rate & amount, total / amount due and payment terms, plus a best-effort line-item table (description, qty, unit price, amount). Every value is shown in an editable panel so you can correct any OCR slip, and you can export the structured result as JSON or CSV or copy it. Everything runs locally, so your invoice is never uploaded — safe for confidential documents. It pattern-matches and does not truly understand the invoice, so review every value before using it for accounting or payment.

Upload a PDF or image — pdfjs reads the text layer, tesseract.js OCRs scans & images, all in your browser
Extracts number, invoice & due dates, PO, vendor, bill-to, currency, subtotal, tax/VAT/GST, total & terms
Best-effort line-item table (description, qty, unit price, amount) — every field editable to correct
Export structured JSON or CSV, or copy — ready for a spreadsheet or accounting import
100% client-side — your invoice is never uploaded; rule-based, so always verify before accounting/payment

What is

Invoice field extraction (OCR + parsing)

Invoice field extraction is the process of turning an invoice document into structured data — the invoice number, dates, vendor, taxes, totals and line items — so it can be filed, exported or imported into accounting software. AnyTool’s Invoice Field Extractor does this entirely in the browser: it reads a digital PDF’s text layer with pdfjs (and falls back to tesseract.js OCR for scanned pages or uploaded images), then applies rule-based heuristics and regular expressions to label the standard invoice fields and to detect line-item rows. Because invoice layouts vary widely and OCR introduces errors, it is pattern matching rather than true comprehension: it assigns each field a confidence, shows everything as editable, and is meant to produce a reviewed draft, not a final accounting record. No document is ever uploaded.

Generators

Related terms

Invoice OCRLine-item extractionAccounts payablepdfjs text layertesseract.jsGST / VAT

Frequently Asked Questions

Open the Invoice Field Extractor and upload your invoice — a PDF or an image such as a JPG or PNG. When you click Extract, the tool gets the document’s text locally: for a born-digital PDF it reads the embedded text layer with pdfjs (fast and exact, no OCR), and for a scanned page or an image it renders the page and runs tesseract.js optical character recognition. It then parses that text with rule-based heuristics and regular expressions, matching labels like “Invoice No”, “Date”, “Due”, “Subtotal”, “Tax/VAT/GST”, “Total” and “Bill To” and reading the amount that sits next to each, plus a best-effort scan for line-item rows. The results appear as an editable field panel and an editable line-item table, each field tagged with a confidence, so you can fix any OCR slip before exporting the structured data as JSON or CSV.

Upload the invoice (PDF or image) and click Extract. The tool reads the text in your browser and lists the number, dates, vendor, totals, tax and line items as editable fields you can correct and then export as JSON or CSV.

The Invoice Field Extractor runs completely client-side. The PDF text layer is read with pdfjs and any scan or image is recognised with tesseract.js — both libraries load from the app’s own bundle, with no server, no API, no AI and no CDN call for your document — and the page works offline once cached. The file you upload never leaves the page; nothing is sent, logged or stored. That is the key difference from cloud invoice-OCR or “AI” extraction services, which upload your documents to their servers and sometimes use them to train models. Because your invoice stays on the device, the tool is safe for confidential or client invoices, and only the JSON or CSV you choose to export ever leaves the page.

No. The PDF or image is opened, rendered and OCR’d entirely in your browser. Nothing is sent to a server, so it is safe for confidential invoices.

It targets the standard invoice fields: the invoice number, the invoice date and due date, a purchase-order (PO) number, the vendor or supplier name and address, the bill-to party, the currency, the subtotal, the tax / VAT / GST rate and amount, the grand total or amount due, and the payment terms (for example “Net 30”). It also makes a best-effort attempt at the line-item table — the description, quantity, unit price and amount for each row. Header fields appear once in fairly predictable places and extract more reliably, while line-item tables are the hardest part because they have many layouts and often no visible borders, so some rows may be missed. Every value is editable and you can add line-item rows manually.

The invoice number, invoice date, due date, PO number, vendor and address, bill-to, currency, subtotal, tax/VAT/GST rate and amount, total/amount due and payment terms — plus a best-effort line-item table.

Accuracy depends heavily on the invoice: a clean, born-digital PDF extracts very well because it uses the real text layer, whereas a low-quality scan or a photo relies on OCR, which misreads characters, and an unusual or multi-column layout can defeat the rule-based matching. Crucially, the tool does not truly understand the invoice — it matches labels and patterns — so it will sometimes miss a field, pick the wrong amount, or merge line-item columns. That is why every field and line item is editable and tagged with a confidence. Use it to do the bulk of the typing for you and to keep confidential invoices on your device, but review and correct each value before you rely on it for bookkeeping, tax or payment. It is not an AI, not an accountant, and not a substitute for verifying the numbers.

Treat it as a fast first draft, not a final record. It pattern-matches with OCR and regex, so accuracy depends on the layout and scan quality — always review and correct every value before using it for accounting or payment.

Upload an invoice (PDF or image) and the tool reads its text entirely in your browser — pdfjs for a digital PDF’s text layer, tesseract.js OCR for scans and images — then parses the invoice number, invoice & due dates, PO number, vendor and address, bill-to, currency, subtotal, tax/VAT/GST, total and terms, plus a best-effort line-item table, into an editable panel you can correct and export as JSON or CSV. Rule-based pattern matching, not AI; nothing is uploaded.

Features

Reads digital-PDF text with pdfjs and OCRs scanned pages / images with tesseract.js — all client-side, no CDN
Extracts number, invoice & due dates, PO, vendor, bill-to, currency, subtotal, tax/VAT/GST rate & amount, total, terms
Best-effort line-item table (description, qty, unit price, amount), with per-field confidence
Every field and line item is editable; add rows manually; export JSON / CSV or copy
100% client-side — invoices never uploaded; rule-based, so review every value before accounting/payment

PriceFree

CategoryGenerator

PlatformAll (browser-based)

PrivacyThe uploaded invoice is opened, rendered and OCR’d locally and never uploaded, logged or stored — safe for confidential invoices

Input: Invoice PDF (digital text layer or scanned), Invoice image (PNG, JPG, WebP, etc.)

Output: Structured JSON (fields + line items), CSV (header fields + line items), Copied JSON or raw text

Detailed Explanation

Methodology

Getting an Invoice’s Text In the Browser: pdfjs Text Layer + tesseract OCR

AnyTool’s Invoice Field Extractor turns an uploaded invoice into structured data entirely on the device, and the first step is getting the text. For a digital (born-digital) PDF it reads the embedded text layer with pdfjs-dist: it walks every page’s positioned text items, buckets them by their Y coordinate to reconstruct lines and sorts each line by X so the reading order is preserved — this is fast and exact and needs no OCR. If a page carries little or no text (a scanned or image-only PDF), that page is rendered to a canvas at 2× scale and recognised with tesseract.js; an uploaded image (PNG, JPG, WebP, etc.) is OCR’d directly. Both pdfjs and tesseract.js load from the application’s own bundle — there is no CDN worker URL and no server — and a live progress bar reports the reading and OCR stages. The result is the invoice’s plain text, ready for parsing, obtained without the document ever leaving the browser.

Digital PDFs use the pdfjs text layer — lines reconstructed from item X/Y positions, no OCR needed
Scanned PDF pages are rendered to canvas at 2× and OCR’d with tesseract.js; images are OCR’d directly
pdfjs and tesseract.js load from the app’s own bundle — no CDN, no server
Per-page detection: a page with too little embedded text is sent to the OCR fallback automatically
A progress indicator shows the reading and OCR stages while everything runs locally

Technical Details

Rule-Based Field and Line-Item Parsing

Once the text is available, the tool parses it with a rule-based engine (regular expressions plus layout heuristics) rather than AI. Label-anchored patterns capture the invoice number, PO number, invoice date and due date (matching numeric, slash/dash and written dates near labels such as “Invoice No”, “Date”, “Due” and “PO”) and the payment terms (for example “Net 30” or “due on receipt”). Money fields are read by scanning the lines for label rows — “Subtotal”, “Tax/VAT/GST” and “Total/Amount Due/Balance Due” — bottom-up (because totals sit near the end of an invoice) and taking the amount that trails the label, with a fallback that picks the largest monetary value as the grand total if no labelled total is found; amounts are normalised so both US (1,234.56) and European (1.234,56) formats become a clean number, and the currency is detected from an explicit code or the most frequent symbol. The vendor is inferred from the first substantial company-like line at the top of the document and the bill-to from its own label. Line items are a best-effort table parse: each line is tested against patterns for “description, qty, unit price, amount” (and a “qty × unit” variant), skipping header and summary rows. Every field carries a confidence (high / medium / low / not found) and is rendered editable, with an editable line-item table and an “Add row” control, then exported as JSON or CSV.

Label-anchored regex for invoice number, PO, invoice & due dates and payment terms
Subtotal / tax / total read bottom-up by label, with a largest-value fallback for the grand total
Amounts normalised across US (1,234.56) and EU (1.234,56) formats; currency from code or symbol frequency
Vendor inferred from the top-of-document company line; bill-to from its label
Line items matched row-by-row (description, qty, unit price, amount) — best-effort, all editable

Privacy & Security

Privacy and the Honest Limits

The whole pipeline — opening the file, reading the PDF text layer, rendering and OCR’ing scanned pages or images, and parsing the fields — runs in the browser. There is no server, no API, no AI/LLM and no CDN call for the document, and the page works offline once cached, so a confidential or client invoice never leaves the device; only the JSON or CSV the user chooses to export ever leaves the page, the tesseract worker is terminated and object URLs are revoked when extraction finishes or the component unmounts. This is the deliberate contrast with cloud invoice-OCR and “AI” extraction services, which upload the document to their servers and sometimes use it to train models. The tool is equally clear about what it is not: it states prominently that it uses in-browser OCR plus rule-based field matching, NOT true understanding — it pattern-matches — so its accuracy depends entirely on the invoice layout and scan quality, and it WILL miss or mis-read fields on unusual layouts, low-quality scans or multi-column or borderless line-item tables (the hardest part of invoice extraction). Because of that, every extracted value is editable and tagged with a confidence, and the guidance is explicit: treat the output as a draft and review and correct every field and line item before using it for accounting, bookkeeping, tax or payment.

Invoice field extraction: AnyTool vs cloud invoice-OCR vs manual entry
Capability	AnyTool	Cloud invoice-OCR / AI	Manual entry
Approach	In-browser pdfjs text + tesseract OCR + rules	Server-side OCR / AI model	Human typing
Input	PDF (digital or scanned) or image	PDF / image	Anything
Fields + line items	Header fields + best-effort line items	Header + line items (often better)	Whatever you read
Editable output	Yes — every field & row editable	Sometimes	Inherently
Processing	Runs in your browser	Uploads your invoice	No software
Privacy	Never uploaded; safe for confidential invoices	Document sent off-device; may train models	Private but slow
Honesty	States it pattern-matches; verify values	Marketing may overstate accuracy	Error-prone, tedious

AnyTool reads and OCRs the invoice locally and never uploads it; it is rule-based pattern matching, not AI — always review the values.

Cite This Page

Title: Invoice Field Extractor (in-browser invoice OCR + rule-based extraction of number, dates, vendor, totals, tax and line items, with editable output and JSON/CSV export)
Author: AnyTool
Published: January 1, 2026
Modified: February 8, 2026
Section: Generators
URL: https://www.anytool.tech/invoice-field-extractor

Formatted Citation

AnyTool. "Invoice Field Extractor (in-browser invoice OCR + rule-based extraction of number, dates, vendor, totals, tax and line items, with editable output and JSON/CSV export)." AnyTool, January 1, 2026. https://www.anytool.tech/invoice-field-extractor

A privacy-first, 100% client-side Invoice Field Extractor that turns an uploaded invoice (PDF or image) into structured, editable data entirely in the browser, with no upload, server, API, AI/LLM or CDN call for the document and offline support once cached, so even confidential invoices never leave the device. It gets the invoice text locally: for a born-digital PDF it reads the embedded text layer with pdfjs-dist, reconstructing lines from each text item’s X/Y position so reading order is preserved; for a page with little or no text (a scanned or image-only PDF) it renders the page to a canvas at 2× and recognises it with tesseract.js, and an uploaded image is OCR’d directly — both libraries loaded from the app’s own bundle, with a progress indicator for the reading and OCR stages. It then parses the text with a rule-based engine (regular expressions plus layout heuristics) drawn from invoice-OCR research on the standard fields: label-anchored patterns capture the invoice number, PO number, invoice date and due date (numeric, slash/dash and written dates) and the payment terms (e.g. Net 30); subtotal, tax/VAT/GST and total are read bottom-up by label with a largest-monetary-value fallback for the grand total; amounts are normalised across US (1,234.56) and European (1.234,56) formats and the currency detected from a code or the most frequent symbol; the vendor is inferred from the top-of-document company line and the bill-to from its label; and line items (description, quantity, unit price, amount) are parsed best-effort row by row, skipping header and summary lines. Every field carries a confidence (high/medium/low/not found) and is rendered editable alongside an editable line-item table with an Add-row control and a collapsible raw-text view, and the structured result can be copied or exported as JSON or CSV. The page is explicit and prominent that this uses in-browser OCR plus rule-based field matching and does NOT truly understand the invoice — it pattern-matches — so accuracy depends entirely on the invoice layout and scan quality, header fields extract more reliably than line-item tables (which are notoriously hard because layouts vary and often have no borders), and it will miss or mis-read fields on unusual layouts or poor scans; the honest, repeated guidance is to treat the output as a draft and review and correct every field and line item before using it for accounting, bookkeeping, tax or payment. On privacy, the file is opened, rendered, OCR’d and parsed only on the device, the tesseract worker is terminated and object URLs revoked when extraction finishes or the component unmounts, nothing is sent, logged or stored, and only the JSON or CSV the user exports ever leaves the page — the deliberate contrast with cloud invoice-OCR and AI extraction services that upload documents to a server and sometimes use them to train models.