r/documentAutomation

👋 Hey n8n community,

I pulled the image-description part out of a bigger product-content workflow and turned it into a small standalone template, since a few people wanted just that piece. It's now up on the n8n library.

What it does:

• Upload one or more product photos through an n8n form.
• Each image is described on its own (looped, so nothing gets bundled into one call).
• The easybits Extractor returns a structured description per image, which works as product copy and doubles as image alt text.
• You get a styled results page with a thumbnail and a copy button per image, plus a clean fallback when an image can't be read.

Template: https://n8n.io/workflows/16901-generate-product-image-descriptions-from-form-uploads-with-easybits-extractor/

The thing I keep wondering: most shops and sites still write image descriptions and alt text by hand. How are you handling it right now, manual, a vision model, or something automated? And if you were taking this to production, what would you add first, bulk upload, direct publish to your shop, multi-language?

Best,
Felix

👋 Hey n8n Community,

I've built a lot of finance workflows over the last few months for friends who run small businesses, and going back through my library I realised I'd never shared the most basic one people keep asking for: a simple batch invoice extractor. So I cleaned one up and pushed it to the n8n template library: Extract batch invoice data from form uploads with easybits and Google Sheets.

The idea is simple. You upload one or many invoices (JPG, PNG, or PDF) through a single form, and it extracts the data from all of them in one go, instead of dragging every invoice in one by one. Each invoice lands as a row in a Google Sheet, and when the batch finishes, the form shows a summary marking every file with a ✅ or ❌ so you instantly see which ones need a second look.

How it's set up:

• An n8n Form takes one or more invoice files.
• The files get split into one item per file, keeping the original filename.
• It loops over the invoices one at a time, sending each to the easybits Extractor, which returns the fields (invoice number, date, vendor, total, and so on) as a structured data object.
• The filename gets reattached, and a check runs over the critical fields.
• One row per invoice is appended to Google Sheets, with a pass/fail status.
• A batch summary is shown as the form's completion message.

A few things from the build that might save you time on your own flows:

• The extractor bundles everything you hand it into one request. Pass it all the files at once and you get one merged result back, not one per invoice. Looping one file at a time is what gives you a clean result per invoice. This one cost me a debugging session.
• Treat "missing" as a signal, not an error. The extractor returns null when a field isn't on the document. Instead of fighting that, I lean into it: a small check flags any invoice missing a critical field, which is what powers the ✅/❌ summary. Worth catching the sneaky empties too (the string "null", empty strings, whitespace), so nothing slips through looking present when it isn't.
• The fields are yours to change. The mapped fields are just a starting point, so you can add whatever you need to pull from your own invoices, like a VAT ID, PO number, or IBAN. The extractor also has auto-mapping, so you can upload one example invoice, let it detect the fields, and tweak from there.

I also recorded a short video showing how it runs end to end, which I'll post alongside this.

For more free workflows, feel free to check my GitHub as well: https://github.com/felix-sattler-easybits/n8n-workflows. A star helps other builders find it, so I'd be really thankful for that support.

How do you all handle the invoices that fail extraction? Curious whether people flag them for manual review like this or route them elsewhere.

Have a good start to the week.

Best,
Felix

👋 Hey n8n community,

A few weeks back I helped one of our users whose data table extraction kept bleeding cells between rows: 95% of the data came out right, but the last 5% landed in the wrong rows, so he could never fully trust it. The way I fix pipelines like that is I never eyeball the output, I build a testing workflow that scores every extraction against a known-good reference so I can benchmark it and see if my changes actually helped.

I shared a first version of that tester, and a bunch of you asked for a v2 that lets you upload your own reference through the same form instead of hardcoding it. So that is what this is.

I also recorded a short video where I run a full test end to end, if you'd rather watch it in action.

How it works:

You upload two things on one form: the document you want to test, and a reference to check it against. The reference can be a CSV (exported straight from Excel) or a PDF/image of the same table. The workflow extracts your document, compares every cell against the reference, and shows a pass/fail card with the accuracy, the mismatches, and how long the extraction took. Each run is logged to a Google Sheet so you can compare engines and track accuracy over time.

The clever bit is trust. A CSV is trusted as-is because a human made it. A PDF reference gets extracted first and shown back to you to confirm before it is used as ground truth, so you are never grading one guess against another.

A few takeaways even if you skip the video:

1. Don't eyeball table extraction. Scoring every cell against a reference tells you exactly which rows slipped, instead of scanning 20 rows by hand.
2. Trust your reference before you trust the test. If your ground truth comes from an extraction too, verify it first, or a "100%" means nothing.
3. When rows slip, refine the descriptions. The easybits Extractor lets you write a description per data field, and that context is usually what fixes it. Tightening a field's description often covers new layouts too, without touching the workflow.
4. Your reference CSV headers have to match your pipeline field names. That one mismatch silently fails every row, so I added a small column map in the workflow to line them up.

Grab the tester here: https://github.com/felix-sattler-easybits/n8n-workflows/blob/e203ef38bc69db58e08e282b18bc287d69d7d85b/easybits-data-table-extraction-testing-tool/easybits_data_table_extraction_testing_workflow.json

It sits in my repo with 20+ other n8n templates I have built with this community: https://github.com/felix-sattler-easybits/n8n-workflows

How do you currently check whether an extraction is actually correct, by hand or with something automated?

Best,
Felix