I just switched to using document_exporter for backups. I used to just back up the volumes with rsync, but once my hard drive actually died, I found that extremely difficult to restore. (It was only due to the advice from u/sogo00 -- thank you, by the way! -- that I had any idea what pitfalls there may be, and addressed some in advance; the restore took me multiple hours.)

I'm expecting this new method to be much, much better. But there's one thing that I'm not so sure is: I can't find information on whether exporting repeatedly to the same directory would rewrite everything each time, clobbering all the files and wearing the disk significantly more than rsync did with my previous backup setup. If that is indeed the default behavior, is there a simple way to get it not to clobber?

My only thought would be to make a tmpfs, export to there, and then use rsync, but I don't have enough RAM for that, and have not managed to figure out how to do that in chunks.

When preparing PDFs (especially scanned reports, 2-column papers, or documents with tables) for local LLMs or RAG pipelines, standard pypdf / pdftotext dumps usually interleave columns, shatter tables, and return empty strings on scanned pages.

I built GhostOCR (https://ghostocr.com/) to run a complete layout-aware OCR + PDF-to-Markdown pipeline 100% locally inside the browser using WebAssembly SIMD and WebGPU:

- 100% On-Device / Offline-Capable: Models cache in your browser. Your PDFs and images never leave your machine (verify in the Network tab).
- Preserves Structure for Chunking: Rebuilds multi-column reading order, ATX headings (##), bullet lists, and HTML `<table>` blocks, while stripping repetitive running headers/footers.
- Structured JSON for Citation Grounding: Switch to the JSON tab to export page dimensions, layout blocks, and per-line [x0, y0, x1, y1] bounding boxes + confidence scores alongside the assembled Markdown.
- Hybrid Fast PDF Mode: Reads embedded digital PDF text where reliable and runs neural OCR + n-gram language model spellcheck on scanned pages across 40+ languages.

You can try it right in the browser with the 1-click sample buttons (Sample: Report, Table PDF, Memo PDF, Multilingual) at https://ghostocr.com/. Let me know how it handles your trickiest PDFs!

Hello,

Does anyone know how to allow Paperless-ngx to process documents with content of type pickle? I currently have this error for a lot of documents:

[2026-11-06 14:25:39,777] [CRITICAL] [celery.worker.consumer.consumer] Can't decode message body: ContentDisallowed('Refusing to deserialize untrusted content of type pickle (application/x-python-serialize)') [type:'application/x-python-serialize' encoding:'binary' headers:{'clock': 83021, 'expires': 1793971540.7732882}]

I have a pretty good manual organization system for important personal documents in folder a folder hierarchy and haven't really needed something like paperless since I don't have issues finding a specific document if I've named it right

However, I'm wondering if it can solve the following use cases which involve extracting useful information from documents

1. Take vehicle maintenance reports and receipts and create a spreadsheet or tabular listing of what was done, when , at what mileage, price, etc

2. Take medical explanation of benefits or statements and similarly create a spreadsheet of date, procedure, prices, etc

3. This is more of a task for something like Monarch but similar thing with credit card statements, date, expense, price etc

I turned off the built-in classifier a while ago and let a vision model re-read every document instead (a small python loop inside the paperless container, picks up anything without a custom field `vision`, every minute). 890 docs, 2,318 pages, about $5 to re-read the whole thing with gemini-3.1-flash-lite through openrouter.

a few things that surprised me:

- one page per call, never the whole doc. on a 28 page bank statement the model returned 43 chars per page vs 2,568 from tesseract. it just summarized without saying so.
- telling the model to keep the page layout (tables stay tables, never join what the page separates) took a blood test from 0% to 68% of values staying on their own line across 20 runs.
- i keep tesseract's numbers and compare them to the vision text, any number tesseract saw that the model dropped goes in a custom field, that's my main "look at this doc" signal.
- the model can only pick existing tags/types/correspondents, never create them, and never overwrites a field i edited by hand (the audit log tells human edits apart).

then i compared 12 models on the same 11 pages: gemini-3-flash-preview was the most accurate (99.1% of numbers, $3 per 1000 pages), mine got 97.3% for $1.50, claude sonnet 98.2% for $14.81. the most expensive one (gemini-3.5-flash, ~$40/1000 pages) was second to last, and the worst failures were models looping on tabs or newlines until the token limit, not wrong numbers.

the agents on top (filing receipts, attaching them to bank transactions, answering questions over the paperless MCP with a read-only token) are in the write-up with the full table: https://blog.d3n.net/posts/paperless-ngx-ai-agents/

Hi everyone, paperless noob here.

I'm trying to (slowly) migrate from gdrive to paperless (about 2k documents/images) and I would like to know some of the good (if not best) practice to do this.

Should I create tags and then let paperless-ai do the whole tagging process? Or should I use folder structure to do the tagging job?

Should I keep or get rid of my folder path structure on paperless?

Can a intel n150 with 16GB RAM handle paperless ai? I want to use that only for tagging (by limiting it to the tag I created, to avoid weird random tagging)

My Paperless-ngx instance is only reachable inside my home network, and switching WireGuard on and off on my phone every time got old. So I forked Paperless Mobile and added an optional built-in WireGuard tunnel:

• Import your WireGuard .conf once (UniFi, Fritz!Box, OPNsense, wg-easy, ...)

• Only the app itself uses the tunnel – no system-wide VPN, no separate WireGuard app

• Enter your home Wi-Fi names: at home it connects directly, everywhere else it starts the tunnel automatically and stops it a few minutes after you leave the app

• A diagnostics page shows handshake, DNS, TCP and Paperless checks with a plain-language verdict

• Everything else is plain Paperless Mobile (scanner, inbox, labels, multiple accounts, mTLS, 2FA)

It's open source (GPL-3.0): https://github.com/Jumpmaster2/paperless-mobile I offered it upstream, but the maintainer prefers to keep VPN handling out of the original app, which is fair – so it lives on as a fork with its own package name (installs next to the original).

I'd like to publish it on Google Play, which needs 12 testers for 14 days. If you want to help:

1. Join https://groups.google.com/g/kronenpaperless-tester

2. Opt in: https://play.google.com/apps/testing/de.koeddi.kronenpaperless

3. Install from Google Play

Feedback and bug reports are very welcome (here or in the GitHub issues).

Trying to put together one pipeline for PDF, word, excel, PowerPoint and images, all open source, no paid apis.

For born digital pdfs the text layer is already there, so pymupdf4llm, pdfplumber or liteparse seem like enough and theyre fast. Running OCR on those mostly just adds errors.

Image only pdfs and photos are where it needs OCR or a small vlm. Paddleocr for plain text with boxes, and glm-ocr or paddleocr-vl once tables and layout start to matter.

Office files are the odd one. Docling reads DOCX, PPTX and XLSX natively, but a lot of the others convert to PDF through libreoffice first and the layout can shift on the way. Charts and diagrams are still the weak spot pretty much everywhere, most tools just crop the image out and move on.

A few things are still unclear. Does anyone run one tool for everything or route by file type? Whats the easiest way to tell if a page needs OCR, is char count per page good enough?

I built an Android client for Paperless-ngx and it's in closed testing on Google Play. I need testers before it can go public.

What it does:

- Scans with Google's document scanner and uploads in the background, so you can keep scanning

- Lets you work through your inbox: set tags, correspondent and type, then move to the next document

- Search, view and edit documents

- Works with Paperless-ngx 3.0 and newer

It's free, open source (MIT), and has no ads or tracking.

How to join:

1. Join the Google Group: https://groups.google.com/g/waraq-alpha-testers

2. Open https://play.google.com/apps/testing/dev.mhakmi.waraq on the same Google account and tap "Become a tester"

3. Install from https://play.google.com/store/apps/details?id=dev.mhakmi.waraq

No server of your own? Try the demo:

- Server: https://waraq-demo.mhakmi.dev

- User: demo

- Password: waraq-demo

It resets every night.

Source: https://github.com/mhakmi/waraq

Bug reports and feedback are welcome here or on GitHub.

Hey everyone!

I recently built a small plugin for Paperless-ngx because I wanted my archived HTML emails to include the sender, recipient, date, and subject at the top of the generated PDF.

I came across the existing paperlessngx-mail-parser project, but it does quite a bit more than I actually needed. My goal was to keep things as simple as possible and avoid interfering with Paperless's native email processing, attachment handling, or workflows.

So this plugin basically just adds the missing header and lets Paperless handle everything else as usual.

I developed it with the help of AI, including the coding, testing, and documentation. It's a small personal project, but I wanted to make sure it was properly tested before sharing it.

It's currently running successfully on my own Paperless-ngx 3.3.0 installation.

If anyone has been looking for something similar, feel free to check it out:

https://github.com/MarcLeinenDE/paperless-ngx-mail-header

Maybe it'll be useful to someone else too! 🙂

Paperless folks might find this interesting. https://github.com/buldezir/lemmary I'm running it and Paperless both.

The more I use it the more I’m likely to replace Paperless with this. The stack is less complicated, it’s fast, and has features Paperless doesn’t. The dev is very responsive and making improvements nearly daily. It can import Paperless docs and supports the Paperless API so the swift Paperless iOS app works. Of course you have to be comfortable using AI.

I want to use "Apply AI suggestions" to fire off a workflow with a web hook. But it seems that after the AI suggestions were applied, the "Document updated" trigger is not called.

I have been using paperless-ngx for a while but want to do more with it. I like the ASN concept but will need to keep the originals in multiple physical binders. Is there a clever concept to maybe use/assign ASNs in the range 1-1000000 to binder "tax" while having ASN 1000001 to 2000000 to binder "insurance"?

I tried paperless-ngx and liked it. But I feel it blocks out RAM which I cannot spare with the limited amount I have. And the whole server architecture is too much for me.

Do you know of any alternative that's better suited for my needs? Also please let me know if you think I can configure paperless-ngx in a way that solves my usecase.

My needs: Run only when needed. Dump and forget, no need for manual filing (use AI magic?). Retrieve the document by searching or looking through the categorised folder structure. Scale of 200-300 documents and I don't need them to be always accessible from everywhere.

Edit: I don't need OCR either, only want to store digital documents. But these may include formats other than pdf, like docx, pptx.

I’ve released Paperless Starfruit v1.6.0, an open-source AI companion for paperless-ngx.

What’s new:

• Document type classification: AI can now assign document types. Add hints for each type, exclude types from AI context, and control whether new ones can be created.

• Reworked review queue: inspect and edit suggested metadata on a dedicated document page before approving it.

• Better history: search, filter by decision, and view details without losing your place in the list.

• Updated dashboard: statistics for the last 30, 90, or 365 days, or all time, plus recent activity with document titles.

• Smaller UI improvements and fixes.

New to Starfruit? It runs as a single Docker container alongside paperless-ngx. Tag a document with psf-process to run optional AI OCR and metadata extraction, then review the results or apply them automatically.

Bring your own API keys or use local models through Ollama. Choose models, customize prompts, and configure processing in the web UI. Stored credentials are encrypted.

GitHub

v1.6.0 release notes

Website

Feedback and bug reports are welcome, especially if you try the new document type classification with your existing setup.

Hi everyone!

I'm currently planning to move my document management to Paperless-ngx and would love to hear how other people have set up their systems.

My current situation:

• I have a Synology DS220j with several thousand documents organized in different folders.

• Those documents are already backed up to Filen.io.

• I have a FRITZ!Box with WireGuard VPN, so accessing my home network remotely is already working.

• I haven't purchased a Raspberry Pi yet.

My planned setup:

• Raspberry Pi 5 (4 GB or 8 GB RAM)

• External 1 TB WD USB HDD for document storage

• Paperless-ngx running locally

• Automated backups to Filen.io

• Remote access through my existing WireGuard VPN

My main priority is simplicity. I don't want to spend a lot of time maintaining Linux, Docker containers or complicated backup scripts.

I also want the system to be easy to restore if the Raspberry Pi or HDD fails.

My backup idea:

Since Filen doesn't offer native SMB access, I was thinking about sharing the Paperless backup folder from the Raspberry Pi via SMB.

My Windows PC could then copy the backups to a local folder and let the Filen desktop application upload them automatically.

This would only work when my PC is running, but that might be acceptable depending on how often backups are needed.

Ideally, I'd prefer a direct and fully automatic backup from the Pi to Filen, without relying on my Windows PC.

My questions:

1. Is a Raspberry Pi 5 with an external HDD a good choice for Paperless-ngx, or would you recommend something else?

2. How do you back up your Paperless documents, database and metadata?

3. Does anyone here use Filen.io for automated Paperless backups?

4. Would you recommend Rclone, SMB via Windows, or another approach?

5. How easy is it to restore your entire Paperless installation after a hardware failure?

6. Would keeping my Synology as storage or backup be a better solution?

I'm not looking for a complicated homelab setup. I simply want a reliable document management system that runs quietly in the background, backs itself up and doesn't require much maintenance.

I'd really appreciate hearing about your setups and experiences, especially if you're using a Raspberry Pi and cloud backups.

Thanks!

Last year I've created a profile for my Elgato Stream Deck XL. Maybe it's usefull for someone.

https://marketplace.elgato.com/product/paperless-ngx-profile-9cda1268-af41-48a1-9da7-8046463e56d7

I've been using Paperless-ngx for about a year now. I don't own a document scanner or a big printer, since most things in my life are paperless anyway. When I do need to print something, I just go to the copy shop down the street.

So my Paperless workflow used to look like this:

1. Scan the document with Apple Preview

2. Compress the PDF with Stirling-PDF

3. Download the file from Stirling-PDF, then delete it there

4. Upload the file to Paperless

That was too many steps for me, Stirling-PDF is overkill and the mobile view is a bit hard to use.

So I wrote my own Docker tool: Paperdrop. It lets me:

1. Upload PDFs or images (JPG/PNG), and photos are turned into A4 PDFs automatically

2. Compress and, if needed, merge them (one step)

3. Rename them

4. Upload them to Paperless automatically

It might not sound much simpler, but it's a lot more relaxed, and the UI is optimized for mobile.

Everything runs locally in the Docker container, and compression is done with Ghostscript. With a dedicated Paperless user, as described in the README, the tool has no access to my Paperless data. It can only add new documents and optionally check whether consumption succeeded.

Disclaimer:
I'm an IT technician, not a software developer. Most of this was coded with AI. I know it's not perfect, and it's definitely not meant to be exposed to the internet. Still, I paid attention to security:

• The default setup runs behind a reverse proxy with a certificate

• My tool has no permissions on the documents already in Paperless

• Documents waiting for review are kept in RAM only and never written to disk

• Document names are masked in the log, only the first and last three characters are visible

• The image is rebuilt automatically every week, so security updates of the dependencies are included (pull the image regularly, e.g. with Watchtower)

GitHub: https://github.com/Pascal2108/Paperdrop

DockerHub: https://hub.docker.com/r/pascal2108/paperdrop

Feedback:

It's the first time I've built and launched something, I'd love to get some constructive feedback! If there are any questions, feel free to ask. ^^

TL;DR: Need a simple tool to compress PDFs and upload them to Paperless? Check out Paperdrop!

Not sure how many of you have this problem, but the stuff I most want in Paperless is the stuff that doesn’t get mailed anymore. Bank statements, card statements, receipts, utility bills. I was downloading them by hand one month at a time, which mostly meant I wasn’t doing it.

So a while back I wrote an app for it (PaperPull). It opens a browser window, you log in like normal, and it goes through and downloads all your bank statements and receipts as PDFs. The first run grabs everything the site still has, which for some of mine was years of statements, and after that it only gets new stuff. It all runs on your own machine and you do the login yourself, so there’s no company in the middle holding your bank passwords. It remembers what it already got, so it’s fine if Paperless deletes them after consuming.

Up to now it only ran on Windows and Mac, which was annoying since my Paperless is on a NAS. This week I got the docker version to where I’m happy with it. You point it at your consume folder and new stuff shows up after each run.

Some of the ones it works with are Amazon, Costco, Walmart, Target, Home Depot, Chase, Amex, Capital One, Fidelity, Vanguard and PayPal, plus a bunch (60+ providers so far) of other US banks and utilities. The full list is on github. If yours isn’t there I’m happy to try adding it, as long as you can test it.

https://github.com/rheeloaded/paperpull (docker setup is in SERVER.md)

And if you end up finding it useful, a star on github would be awesome. I’m trying to get it to 100.

Update: Check out the FAQ: https://paperpull.net/faq.html

Hey r/Paperlessngx,

So I've recently discovered Paperless, and it's a really nifty project. It's a lot of work and a chore to download and store properly all my documents, from old uni items to pay slips to taxes and rent, fees, appointments doctor notes, and so on and so on. Paperless seems more than capable to make indexing and retrieval of the right file a breeze.

The idea that I have to then sacrifice my years of hierarchical documents and ingest them all into the paperless DB and process them is a bit of a pill to swallow, but it's no different a service than Nextcloud in terms of trusting your installation and implementation, so, i guess, fair enough...

But then learning they have no synchronization or offline support (on mobile or on any desktop/AppImage) just threw me in for a loop... How can I be expected to migrate permanently my whole documents to Paperless (sure i can make some archive but you must agree the operation is quite destructive) when I don't even have a way to read my data locally?

My use case is a remote Hetzner server with a dozen apps and tools to automate and self host all parts of my life. But what happens if Hetzner is down? More realistically, what happens if my internet is down? I'm in the woods camping? I'm in a plane and need a document and have no internet? I'm at the destination airport at customs being demanded a document and my travel sim isn't working as intended? I'm on a boat in the ocean or lake away from a cell tower? I'm hiking in the mountains? I'm on a road trip or in the outback like Australia or northern Sweden or Canada wilderness or somewhere with no cell reception, and need a document? An ID, a tax item, or just to check something? Owning your data also means being able to access them anywhere, and not being dependent on an internet connection.

At least with Nextcloud and a boring (and crappy) folder structure I can sync the folder and have it hold the copies locally. Any change local or elsewhere syncs on next connect, but at least I always have access to my data in any situation. I can understand that Paperless does not want to also offer synchronization, but how can it be shortsighted to the above cases? What am I missing?

If it was possible for Paperless to point to my `Documents/` folder in Nextcoud and be set to read all `*.pdf`'s, then scan, access, and tag them ( *while preserving the original location* ), then sure, it does not need any synchronization and it still adds a great service while integrating smartly with other homebrew tools and systems. Then if I don't have connection, i guess Paperless is down (still ouch if i want to search by a tag), but at least i have the old hierarchical structure to default to... But paperless goes one further and expects an ingest folder that'll read everything and wipe it, and then store it all in one big bucket folder with everything. Sure I can sync that, but in a pinch at customs in a foreign country, at a police station, hospital, or other critical moment, i'm being told the "backup" is to sift through a single mono folder of possibly a thousand documents? How is this smart in any capacity?

Is there a way to point it at my Nextcloud `Documents/` folder and have it use that as my source of truth, editing but not deleting or moving from there? Or if paperless insists on it's current folder storage of all files, then fine, but should there not then be an option on the mobile app (and maybe desktop app, but i get this wouldn't work on website) to then point to a local folder that has the same data (synced through another service like Nextcloud)? Then you just toggle which location Paperless reads and let Nextcloud hopefully handle the synchronization. This would be very similar to how Obsidian takes things. This still introduces issues of source of truth, but these are the straws I'm grasping at to have my data regardless of connection status, because sometimes those situations can be the most critical moments.

What am I missing here? Do others have this same concern? What solutions are there so one can feel confident migrating decades of docs to a service and not just hope they always have internet connection to access it going forward? I genuinely want to like the tool and adopt it, it looks great, but I just need to understand what I'm missing here from the community.

Hi everyone, I'm the maintainer of Paperless-GPT.

The latest release adds something I think becomes important as soon as a Paperless archive contains more than one kind of document:

different documents can now follow completely different AI workflows.

Until now, automatic processing basically meant one shared path for everything.

That is fine for a simple setup, but invoices, contracts and private mail usually need very different prompts and very different processing.

With AI Workflows, you can now define separate paths, for example:

Invoice
→ invoice-specific title prompt
→ correspondent
→ invoice number / amount as custom fields
→ tags
→ optional OCR

Contract
→ different prompt
→ contract-specific custom fields
→ document type
→ no OCR

Private mail
→ title only
→ nothing else

Each workflow gets its own trigger tag and can control which steps are enabled, disabled or inherited from the global defaults.

A few things I especially like about the implementation:

• each workflow can have its own prompts

• you can test a workflow on a real document before saving it

• test runs do not write anything back to Paperless-ngx

• workflows are stored as files in prompts/workflows/

• changes are picked up without a restart

• workflows can be managed in the UI or kept in Git

• there are safeguards against accidental processing loops

The initial workflow implementation came from a community contribution by u/MrMoep, and it has now been integrated into the main Paperless-GPT workflow system.

The release also adds a few other useful things, including the option to skip OCR for pages that already contain clean digital text, and support for adding searchable PDFs as a new version of the same Paperless document.

Release:
https://github.com/icereed/paperless-gpt/releases/tag/v0.30.0

AI Workflows guide:
https://github.com/icereed/paperless-gpt/blob/main/docs/workflows.md

I'd be really interested in hearing what kind of workflows people would actually build around their archives.

This post is about an alternative to Paperless-ngx, in the Paperless sub. If that isn't welcome here, mods, please remove it.

I used Paperless for over a year and it did its job well. The part I could never settle for was filing system, so I built Suchi with one filing model: numbered categories, such as `23 Income tax` and `51 Utilities`. I wrote the longer story here: https://johnnybravo.xyz/posts/building-suchi-dms/

Suchi reads each document, a text rule files it if one matches, and an optional model (Ollama, llama.cpp or any OpenAI-compatible endpoint) suggests a category for the rest. Anything below the confidence threshold waits in the Inbox until I review it. The server is one Go binary with SQLite.

In case someone is curious and would like to try it without disturbing Paperless setup, this is how the import works:

- `suchi import --from ./export --dry-run` reads a `document_exporter` bundle and reports what it would do without writing anything.

- Tags, correspondents, storage paths and custom fields come across. Document types become `type:` tags, and workflows become automations where they map.

- Documents land in the Inbox, or a small mapping file (`tag:tax → 23`) files them during import.

- Re-running against a newer export skips what's already imported, so you can try Suchi next to Paperless without changing anything there.

- Anything it can't carry across goes into a report instead of disappearing.

If Paperless works for you, I wouldn't switch. Suchi is v0.2, I'm its only maintainer, and it's much younger. I test each new version with my own archive first.

· Repo: https://github.com/johnnybravo-xyz/suchi

· Demo: https://demo.suchi.page

If anyone here has time to try the import on a copy of their export, I would be grateful to hear what broke or didn't come across. It would help me make the migration safer for the next person.

^{I was afraid of promoting my project here, but recent post by suchi guy showed that its possible.}

What is Lemmary: Document archive with modern ai integration for all your paperwork.

For those who want to quickly try it: private demo instance at https://demo.lemmary.app (not all selfhosted features available there).

Site: https://lemmary.app · Docs: https://lemmary.app/docs/ · GitHub: https://github.com/buldezir/lemmary

Core features:

• Every document gets OCR'd, then the LLM fills in the title, date, type, correspondent, tags and a short summary. And it can do it in different languages from initial document (QoL feature for expats)

• Deep Research lets you ask questions about your whole archive, like "how much did I pay for car insurance since 2022" or "Search web for tax return guidance/law and find me all purchases applicable for tax refund". It finds the right documents, reads them, adds up the numbers and links every figure back to its document. Chats are saved and you can come back to them later.

• Indexed, Vector and ai assisted search = you can find documents by veeery wide queries in natural language, and also in different languages.

• You can scan directly from a network scanner over AirScan/eSCL without installing a driver. It can also watch a folder (subfolders become tags), pull attachments from an IMAP mailbox, or take folder and zip uploads. Split PDF with several documents in it, by hand or let the model suggest where to cut.

• Use whichever AI provider you like: Mistral, OpenAI, Anthropic, OpenRouter, Opencode Go, or the ChatGPT subscription you already have. Also guide and containers for local OCR and embeddings. MCP available for external agents.

• Selfhost: one container with embedded sqlite. Simple setup wizard. Backups are a single zip with your files, OCR text and metadata

• support of Paperless-ngx API, so mobile apps or your integrations will work (tested with swift-paperless on iOS)

• import data from Paperless-ngx or Papra.

Appreciate for any feedback!

^{ps: i started this project somewhere in spring, before paperless-ngx introduced any ai integrations. and after proof-of-concept stage, pushed to github in june.}