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Florence-2 OCR, tested on a real scanned PDF

1,887

of 1,955 scan words, read in order

Get the setup

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Ten scanned pages in 158 seconds

158 s for 10 pages, Mac GPU

1.6 GB model download

  1. Run pip install torch transformers and brew install poppler (for pdftoppm).
  2. Save florence_ocr.py below next to book.pdf.
  3. Run python3 florence_ocr.py. It writes book.txt. The first run downloads Florence-2-large.
florence_ocr.py 
"""Florence-2 OCR on a PDF: book.txt plus page-NN.txt (brasspage.com/florence-2-ocr/, Nov 12, 2026)."""

import argparse
import glob
import subprocess

import torch
from PIL import Image
from transformers import AutoProcessor, Florence2ForConditionalGeneration

ap = argparse.ArgumentParser()
ap.add_argument("--model", default="florence-community/Florence-2-large")
ap.add_argument("--task", default="<OCR>")  # or <OCR_WITH_REGION>
ap.add_argument("--strips", type=int, default=1)  # cut each page into N bands, top to bottom
a = ap.parse_args()

dev = "mps" if torch.backends.mps.is_available() else "cpu"
model = Florence2ForConditionalGeneration.from_pretrained(a.model, dtype=torch.bfloat16).to(dev)
processor = AutoProcessor.from_pretrained(a.model)
subprocess.run(["pdftoppm", "-r", "200", "-png", "book.pdf", "page"], check=True)


def read(image):
    inputs = processor(text=a.task, images=image, return_tensors="pt").to(dev, torch.bfloat16)
    ids = model.generate(**inputs, max_new_tokens=1024, num_beams=3)
    raw = processor.batch_decode(ids, skip_special_tokens=False)[0]
    out = processor.post_process_generation(raw, task=a.task, image_size=image.size)[a.task]
    return "\n".join(out["labels"]) if isinstance(out, dict) else out


pages = []
for png in sorted(glob.glob("page-*.png")):
    page = Image.open(png).convert("RGB")
    w, h = page.size
    cuts = [round(h * i / a.strips) for i in range(a.strips + 1)]
    text = "\n".join(read(page.crop((0, top, w, bottom))) for top, bottom in zip(cuts, cuts[1:]))
    with open(png.replace(".png", ".txt"), "w") as f:
        f.write(text + "\n")
    pages.append(text)
with open("book.txt", "w") as f:
    f.write("\n\n".join(pages) + "\n")

It is the model card's code, with the <OCR> task, one page at a time.

The base model glues lines together

Scan words in order, of 1,955
Setup Words Time
Large, default 1,887 158 s
--strips 2 1,901 160 s
OCR with region 1,893 195 s
Base model 1,410 123 s

Florence-2-base wrote no line breaks at all. The last word of each line ran into the next: "thename", "oldfellow".

Its model card says the OCR task writes line breaks. Only the large model did.

It rewrites dialect words

The book has dialect. Florence-2 wrote the plain word instead:

On the page Florence-2 wrote
whar what
nary navy
wuth with
anythink anything

19 of the 62 missed places have a double quote mark. A few came out as « or ».

German: words that are not there

On a German two-column page, the true text says:

im Kriegsministerium an den neuen

Florence-2 wrote:

im Kriegsstortrum im Neuen neuen

Every image is resized to 768 pixels square. --strips 3 cuts each page into three bands, so the letters get bigger.

3 German pages: 206 umlauts, 1,887 words
Bands Umlauts Words
1, default 33 341
2 59 807
3 80 989

Other vision models read more, faster

The same 10 scanned pages, scorer and Mac GPU:

Scan words in order, of 1,955
Model Words Time
glm-ocr, Ollama 1,953 104 s
GOT-OCR 2.0 1,936 134 s
Florence-2-large 1,887 158 s
docTR 1,869 13 s

In an 11-page file with a typed cover page, it read all 13 cover words. The scan pages came out byte for byte the same.

Florence-2 and our Actor: 1 win, 4 losses

Our Actor, from a file URL, with no install:

curl -s -X POST "https://api.apify.com/v2/acts/brasspage~pdf-ocr-to-markdown/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"documentUrls": ["https://api.apify.com/v2/key-value-stores/sTnmNrGoSsi7tLQvs/records/anythingllm-mixed-11-pages.pdf"]}' \
  | jq -r '.[].markdown' > mixed.md
Same files, Florence-2 on the Mac GPU
Test Florence-2 Actor
Scan words in order 1,887 1,874
Typed cover words 13 13
Umlauts 80 192
Two columns, in order 279 954
Time, 11 pages 169 s 133 s
Time, 3 German pages 165 s 59 s

Florence-2 used --strips 3 for German.

The Actor needs no GPU and no model download. It costs $0.006 a page.

MIT license, from Microsoft

The Florence-2 license file is MIT, from Microsoft. I read it and the model cards in full before the download.

I used the transformers port in florence-community. Its cards say mit and link to the same Microsoft license file.

How I tested it
  • Scan: the 10 pages of OCR in n8n, proofread Wikisource book scans, 1,955 true words. The 11-page file adds the 13-word typed cover page of AnythingLLM OCR.
  • German: the 3 pages of Paperless-ngx OCR, 1,887 words, 206 umlauts. One page has two columns, 972 words.
  • Score: true words found in order in book.txt, with the scorer of the tessdata test. German: the scorer of Tesseract German.
  • transformers 5.22.2 and torch 2.15.0, Python 3.12, on an Apple Silicon Mac GPU, at the lowest priority. Model revisions 4271c66 (large) and 00921df (base).
  • Times count the whole script, with the model load. The models were already downloaded. One run of each setup.
  • The glm-ocr, GOT and docTR rows: the same files and scorer, on the same Mac, on November 12. They have their own pages.
  • The Actor: two real runs earlier the same day, build 0.1.65, on the same files.
What I did not test
  • CPU only. The script falls back to it without a Mac GPU.
  • The fine-tuned models (-ft) and the original Microsoft code.
  • Bands that overlap, and more than 3 bands.
  • Windows, Linux and NVIDIA GPUs.
  • Handwriting, receipts, tables and photos of signs.
Sources

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Questions people ask

Is Florence-2 good for OCR?

On clean English book pages, yes: the large model read 1,887 of 1,955 words in order. On German pages, it wrote words that are not on the page.

Can Florence-2 read German?

Not well. By default it wrote 33 of 206 umlauts, and words that are not on the page. Page bands help, but not enough.

Is Florence-2 free for business use?

Yes. The license is MIT, from Microsoft, and the transformers port uses the same license.

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