use case
Digitize your notebook without typing it up.
Frenchie reads handwritten pages — notebook scans, whiteboard photos, meeting jottings — and returns Markdown your agent can search, summarize, and reference.
the problem
Why this is a pain.
You take notes in a paper notebook. At the end of a week, you have 30 pages of handwritten jottings and no way to search them. Your agent can read the scan as an image but loses half the content. Typing them up is a tax you keep meaning to pay.
Handwriting OCR has gotten much better, but most general-purpose OCR tools fall back to print recognition and mangle cursive. Note-taking apps with OCR lock you into their ecosystem. You wanted plain Markdown in your own workspace.
the workflow
How Frenchie handles it.
- 1
Photograph or scan your notebook pages. Phone photos work.
- 2
Drop them into your agent. Batch multiple pages at once if that's how you journaled this week.
- 3
Your agent calls ocr_to_markdown via Frenchie for each page. Handwritten text comes through as best-effort Markdown — clean formatting for the parts the OCR is confident on, slightly fuzzier for scrawl.
- 4
Your agent indexes or summarizes the notes however you want. Weekly review, topical extraction, searchable archive — all downstream of having the Markdown in hand.
example
What it looks like in practice.
Your prompt
/ocr ./notebook-scans/week-of-2026-04-13/*.jpg Summarize what I committed to this week, and flag anything I wrote down but haven't acted on yet.
Agent response
12 pages transcribed. Markdown saved per page under .frenchie/week-of-2026-04-13/. **Commitments captured**: - Reply to Maya's email about the Q2 roadmap (noted Monday, status: ✓ done Wednesday) - Draft the proposal for the infra refactor (noted Tuesday, status: not started) - Call Dad about the trip (noted Wednesday, status: ✓ done Thursday evening) - Follow up with the Antigravity team on the install flow (noted Friday, status: not started) **Unactioned items from your notes**: Infra refactor proposal and the Antigravity follow-up. Both flagged for Monday.
What lands in your workspace
$frenchie ocr methodology.pdf
# Section 4 — Results The collected dataset confirms the hypothesis across all three runs. | Run | Accuracy | Latency | | --- | -------- | ------- | | 1 | 94.2% | 118ms | | 2 | 95.0% | 121ms | | 3 | 94.7% | 119ms | *Figure 3.* Distribution shifts across the validation cohort.
tips
Things worth knowing.
- Handwriting OCR quality tracks how legible your writing is. Clean block letters come through ~95% accurate; frantic cursive drops to 70-80%.
- Shadow and glare are the biggest issues for phone scans. Use your phone's built-in document scanner (iOS Notes, Google Drive scan) — they auto-correct perspective and contrast.
- Cost is 1 credit per page. A 200-page notebook runs $2. Cheaper than a replacement notebook.
questions
Common questions.
How accurate is handwriting recognition?
For neat print, very accurate. For cursive or abbreviated scrawl, usable but not perfect. Treat the output as a first-pass digital copy, not a verbatim transcript.
Can I OCR diagrams and sketches?
Frenchie extracts handwritten text. Sketches and diagrams come back as extracted figures (PNG files). Your agent can describe them via its vision capability or you can view them directly.
Does it handle multiple languages?
Yes — Thai, Japanese, Chinese, Arabic, French, Spanish, and many others. Mixed-language notes work.
Can I make my whole notebook searchable?
Yes — your agent takes the Markdown and drops it into whatever search system you use (Obsidian, Notion, a local ripgrep workflow). Frenchie is the digitization step; indexing is downstream.
Try it with a real file of yours.
100 free credits on signup. No card. Drop a PDF or image from your own workflow and see the Markdown your agent gets back.