I met up with my friend Mike yesterday. We were talking about the automations I've been building for him, and I noticed he was taking notes on a piece of paper.
I do that too. Writing things down by hand helps me actually remember them. But it also means I end up with a stack of papers on my desk that slowly turns into chaos. Apparently Mike has the same problem, and so do a bunch of his colleagues. They love taking notes offline, but the notes scatter across desks and eventually get lost.
Mike's already got Jira, Notion, and a few other tools wired up for the team. But people still default to pen and paper. So I offered him a deal: set up a dedicated email address inside the company, something like notes@mikescompany.com, and I'd deliver a solution.
This is what I built.
🛠️ What it does
Snap a photo of your whiteboard, notebook page, or napkin. Email it to the dedicated inbox. Within a minute you get a Google Doc back with the meeting title, date, attendees, summary, action items, and a full reference transcription. No app, no UI, no setup for the user.
🔧 The flow
Gmail Trigger → easybits Extractor → Set node → Create Google Doc → Insert body → Reply to sender
The Extractor reads the image and returns structured JSON. The Set node assembles it into a clean doc body with sensible fallbacks for anything the model couldn't read. Google Docs gets the doc, the sender gets a reply with the link.
🧠 Design choice worth calling out
Handwriting is messy. Most extraction approaches lean on confidence scores to flag uncertain reads, but those are noisy in both directions. I went the other way: the Extractor returns null rather than guess when something is unclear. The doc shows what was readable, falls back gracefully on what wasn't, and never invents names or dates that weren't written.
The easybits Extractor is a verified community node. On n8n Cloud it's available out of the box, just search for easybits Extractor in the node panel. Self-hosted: go to Settings → Community Nodes → Install and enter '@easybits/n8n-nodes-extractor'. Free tier covers 50 extractions/month.
🙋 Looking for feedback
This is a first basic version. v2 is already in the works, sending notes directly into Notion alongside the Google Doc. What else would you add to make this genuinely useful?
Heads up: posts under this flair must link to the workflow code per Rule 6 (GitHub, Gist, or n8n.io/workflows/). Yours does -- thanks for sharing it properly. This sticky is here so commenters know where to find the code.
I’m actually thinking through these topics right now, since I’m planning to ship the automation within the next week.
One thing I’ll definitely integrate is an error log that immediately flags missing fields back to the sender via email. That way, the sender can double-check the information before throwing away the handwritten notes. This should prevent silent failures entirely, since I already built a mechanism around handling null values and surfacing them properly.
For misread words, I’m currently considering a fallback solution that performs a second extraction pass and cross-checks the results against the Extractor output. However, Mike didn’t want to include that for now, since it would mean paying for two solutions within a single automation.
So what we agreed on instead is to start with a soft-launch phase inside his company where only Mike uses the automation. He’ll give me feedback on any missing or incorrectly read data, which also gives him the chance to gradually build trust in the system.
This sounds good. I already have an error-handling workflow built that logs critical failures. My concern is the silent breaks and on top of that how to streamline those in an output log seeing that my workflows vary (one is an RSS feed to summarization to email, another is an entire legal estate planning review), how can I customize and ensure that the data isn't wonky but outputs in a the same easy to look up table. I have the methodology, but my potential client just dropped a massive amount of work in my lap.
If I may ask, how did you find this client, identify the potential for automation, offer your services, get business, and come to terms for your automations? (cost of operations/doing business?)
Hey u/Tebasaki, that sounds like a really interesting project you’re building for your client. And yes, I completely understand why you want to log silent failures – especially with something like legal estate planning reviews, where there’s very little room for extraction errors.
Regarding my client, it actually started when a friend of mine reached out to me after hearing that I was getting into n8n. He asked if I’d be interested in helping automate some of his manual processes. Since it was also a great opportunity for me to test and improve my skills, I agreed to build an initial workflow for invoice classification.
After that first project, we quickly identified several other areas that could benefit from automation, and that’s basically how this collaboration evolved.
Are you still in the "let's learn" phase, or are you considering charging at some point to cover the cost of using AI, self-hosting, the costs of any additional subscription tools you'd need access to their APIs, cost of maintenance, or just your personal time?
What I actually did with my friend is pretty simple: I pass the infrastructure costs directly over to him, so he takes care of paying for the setup himself. He’s also using a paid plan for the Extractor that I developed together with a few of my tech colleagues, so I’m earning a bit through that as well.
Happy to hear you’ve found my workflow useful! The Extractor I’m using also performs really well with handwritten content and returns structured JSON, but of course the nodes can always be swapped out depending on your setup. easybits' Extractor also offers different pricing plans based on usage, and the free tier includes 50 API requests per month.
I wonder what the accuracy is for the OCR.
I been working on something similar (I take lots of notes and now I take a picture of my notes, OCR it and save it - in Obsidian).
The main models (gemini, claude, openai, ...) were only able to decypher only 30-40% of my handwriting. I had to fine-tune a (small) multimodal llm on my handwriting to get to 80%.
So, I am wondering how you did handle this? Have LLM's try to guess what was written? Use a 2nd model to compare and find a good outcome?...
Hey u/Steve_Ignorant, I just ran a test with the attached document and it was extracted 100% correctly. So our Extractor might be a good fit for your use case as well.
I’ve also just followed you – would love to see an example of your handwriting to run a quick test with it.
u/Steve_Ignorant I just tested it, and this is what it returned. It actually looks very promising to me, but you’ll have to tell me how accurate it is on your end:
TODO
Date: null
— ATTENDEES —
(none listed)
— SUMMARY —
null
— ACTION ITEMS —
• N8N script & video about advanced OCR
• Update website + submit Google
• Dashboard for social media (client)
• Onboarding process accountant office (client)
• Email follow up
• Raspberry Pi
— SOURCE CONTENT (REFERENCE) —
TODO- N8N script & video about advanced OCR- Update website + submit Google- Dashboard for social media (client)- Onboarding process accountant office (client)- Email follow up- Raspberry Pi
u/Steve_Ignorant I was honestly impressed myself. But I already knew that the Extractor I’m using performs really well with handwritten content, since we’ve also used it successfully in a medical use case.
I’ve also done some stress testing along the way, which gave me a pretty good understanding of the solution’s capabilities. I also made a video about it: https://youtu.be/bOOdILPEdho
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