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Accurate OCR for Messy Scans
Users struggle to reliably and efficiently extract text from degraded, handwritten, or complex scanned documents because current OCR tools are inaccurate, slow, or hard to customize.
In their words
non solo comprare uno scanner e caricare PDF, ma costruire una pipeline vera che dimostri cosa l'AI può fare end-to-end
I know ScanSnap Home includes built-in OCR, but I find its OCR quality pretty poor
Are any of them reliable enough to save time over doing a manual transcription from scratch?
all questionnaires were filled in on paper (due to the setting), leaving me with an extensive amount of manual work (data-input into excel & spss)
tesseract lai nai train garna khojeko not working
Logic that takes into account sudden unrealistic changes in data (bumped OCR camera or misread data) and maybe hold off an update of the data?
if the OCR wasn't good enough, everything after it became unreliable
Tens of thousands of pages of text received weekly at my organization is this a good model that can do OCR overnight for the billing team?
Ten operator-corrected crops per book (element-wise median residual) took pass@80 from 0.71 to 0.83 on held-out volumes.
Had a client take a picture of their W-2 where the W/H was unreadable, so I asked them to resend and they sent the same picture again.
A few months ago my boss charged me with a massive document OCR-and-restructure task involving hundreds of thousands of documents.
I had a supplier who refused to send excel files, and instead would print out their spreadsheets, scan them, and email the scanned copies.
That is the model I've been using for OCR entry of handwritten datasheets (JPGS of datasheets, structured JSON output).
pulling text out of error screenshots and stack traces that get dropped into Slack as images instead of text, so I can actually search or paste
Document processing teams
Where they talk
- r/computervision
- r/LocalLLM
- r/Rag
- r/LocalLLaMA
- r/Paperlessngx
- r/documentAutomation
- r/Accounting
- r/AI_Agents
Tools they use today
- Tesseract
- PaddleOCR
- Qwen
- Claude
- Paperless-ngx
- Adobe Acrobat
- Gemini
- OpenCV
Why those fall short
- no amount of microfilm adjusting can make it legible
- improved OCR only stored internally, not written back into PDF
- Tesseract misses colored/overlaid text; plugin install is a blackhole; no straightforward replacement example
- Broken strokes, specular glare, low contrast defeat generic OCR models
- model cannot infer per-book inset preference from pixels alone
Map of ideas
Drawing the map…