Local Ai
I built a Windows document archiver that does the recognition locally (own project)
I built a tool I needed myself: ORDNA reads a scan folder or collects attachments from one or more mailboxes, works out what each document is, names it by date and issuer and files it into ordinary Wi
I built a tool I needed myself: ORDNA reads a scan folder or collects attachments from one or more mailboxes, works out what each document is, names it by date and issuer and files it into ordinary Windows folders as PDF/A with a text layer. The point for me: recognition runs on your own PC, with a local model (Ollama with llama3.1). No cloud, no account. For payslips and medical bills I wanted it that way. What it is not: not a Paperless-ngx replacement. No server, no Docker, no multi-user, no access from other machines — a single-user program for one Windows PC. If you want a server solution, Paperless is the better fit; ORDNA is for people for whom that is too much. Limits, so they don't surprise you: it downloads about 4.6 GB of model data once — the installer can fetch it right away. It needs at least 8 GB of RAM (16 GB recommended) and a GPU; recognition then takes a few seconds per document, 10 to 20 for a scan. The installer is not code-signed, so SmartScreen will warn; the SHA-256 checksum is on the page. It is an early version. Free, no time limit, no account: https://zuentec.de/en/ordna.html?utm_source=reddit-ollama Feedback welcome — especially the uncomfortable kind. submitted by /u/ZUENTEC [link] [comments]
Related
- I built a desktop agent for Ollama — reads files, runs commands behind permission gates, vision + image gen, all local (MIT, open source)
- I've been building a local Windows AI document assistant and wanted feedback on whether this solves a real problem.
- I took a local OCR model's accuracy from 60% to 99%
Source: r/ollama | 2026-10-18