Japanese and Chinese documents are where OCR pipelines diverge the most. This page shows a measured comparison on real public documents, run on identical inputs, with raw outputs saved and every step reproducible below. Per our publication rule, we only report on tools we actually ran: Tesseract 5.5.1 (the standard open-source OCR, with jpn/chi_sim models), plain text-layer extraction (pypdf 6 / pdftotext — what most "PDF to text" libraries do), and the pdfToMarkdown API (July 2026). LlamaParse, Mistral OCR, Unstructured, and Marker have not been run yet and nothing on this page makes claims about them.

Test set

Three pages from two real, publicly available documents. To make this a true OCR test, each page was rasterized at 200 DPI and re-embedded as an image-only PDF — a simulated scan with no text layer (verified: 0 extractable characters).

JA prose — Bank of Japan, Outlook for Economic Activity and Prices, April 2026, page 2. Dense Japanese paragraphs, section headings, two footnotes.
JA table — same document, page 10: the Policy Board members' forecast table (7 rows × 4 columns of ranges and medians).
ZH prose — National Development and Reform Commission, 2026 Report on National Economic and Social Development, page 3. Simplified Chinese prose with numbered headings.

Japanese prose test page — Bank of Japan outlook report page 2 — JA prose

Japanese table test page — Bank of Japan forecast table page 10 — JA table

Chinese prose test page — NDRC 2026 report page 3 — ZH prose

Results: character accuracy

Character error rate (CER) against the original documents' digital text layer as ground truth — lower is better. Both texts were normalized identically before comparison (NFKC, all whitespace removed, markdown/HTML syntax and the demo watermark stripped); distance is Levenshtein.

Test case	Text-layer extraction	Tesseract 5.5.1	pdfToMarkdown
JA prose (1,015 chars)	nothing (0 chars — scans have no text layer)	0.99% (10 errors)	0.10% (1 error)
ZH prose (616 chars)	nothing	0.97% (6 errors)	0.00% (0 errors)

The error types matter as much as the counts: Tesseract's errors are wrong characters in real words — it read 婴幼儿 (infants) as 枫幼儿 and 影響もあって as 影響もああって. Those survive spell-check, poison search indexes, and mislead LLMs reading the text downstream. pdfToMarkdown's single error across both pages was a footnote-marker punctuation difference (2,3 → 2.3).

Results: structure

CER only measures characters. For RAG and document automation the harder problem is structure — and on the forecast-table page the difference is not a percentage, it's usable vs not. Tesseract (raw output, excerpt):

2025<2028 年度の政策委員の大勢見通し

対前年度比、9%%。なお、く >内は政策委員見通しの中央値。

実質GD P         消費者物価指数          (参考
コ       消費者物価指数
(除く生鮮食品)     (只く生鮮食品・エネルギー)
+1.0 て +1.0

2025 年度                          +2.7            +3.0
ぐ+1.0>

1 月時点の見周し   +0.8

Column headers merge into free-floating lines, values detach from their rows, and ＜＞ brackets become く > / ぐ >. There is no reliable way to reconstruct which number belongs to which year. pdfToMarkdown returned the same page as a complete table (excerpt, abridged with …):

# 2025～2028年度の政策委員の大勢見通し

<table>
  <tr><td></td><td>実質GDP</td><td>消費者物価指数(除く生鮮食品)</td><td>…</td></tr>
  <tr><td>2025年度</td><td>+1.0 ~ +1.0 &lt;+1.0&gt;</td><td>+2.7</td><td>+3.0</td></tr>
  <tr><td>1月時点の見通し</td><td>+0.8 ~ +0.9 &lt;+0.9&gt;</td><td>+2.7 ~ +2.8 &lt;+2.7&gt;</td><td>…</td></tr>
  <tr><td>2026年度</td><td>+0.4 ~ +0.7 &lt;+0.5&gt;</td><td>+2.8 ~ +3.0 &lt;+2.8&gt;</td><td>…</td></tr>
  …
</table>

（注1）「大勢見通し」は、各政策委員が最も蓋然性の高いと…

All 28 cells present, every range and median attached to the right year and column, headings and the explanatory footnotes preserved around it. The JA prose page likewise came back with its section headings as markdown ## headings and both footnotes intact — Tesseract returns flat text with no structure.

Published results for the underlying model

pdfToMarkdown runs PaddleOCR-VL-1.5 — we've written before about why we built on it. That means you don't have to take only our three-page test above as evidence; the model has published results on much larger academic benchmarks:

94.5 overall on OmniDocBench v1.5 — state of the art at its release, as reported in the PaddleOCR-VL-1.5 technical report (January 2026). OmniDocBench is a third-party document-parsing benchmark from OpenDataLab spanning hundreds of real pages — books, papers, financial reports, newspapers, exams — with a large share of Chinese-language documents, which is exactly the regime this page is about.
Real5-OmniDocBench (same report) — a robustness evaluation on physically degraded inputs: real scans, skew, warping, screen photographs, and uneven illumination. Relevant here because our test above uses clean 200 DPI rasterizations; the published results cover the messy cases ours doesn't.

Attribution matters: those scores are reported by the model's authors (Baidu), on the model in isolation. Our API wraps the model in a conversion pipeline (PDF rasterization, page handling, markdown assembly), so end-to-end behavior is what the measured test at the top of this page checks — the published numbers and our hands-on run point the same direction.

Methodology & limitations

Single run per tool, one page per test case — this is a small, transparent sample, not a large corpus study.
Scans were simulated (200 DPI rasterization). Real scanner artifacts (skew, noise, bleed-through) are not covered here.
Ground truth is the original PDFs' digital text layer, itself imperfect around decorative characters; normalization details above.
pdfToMarkdown was called through the public demo tier — the same thing you can run yourself, no cherry-picking a private build.
Tesseract was run with default settings. Tuned PSM modes may do somewhat better on prose; they do not fix table structure.
Raw outputs are archived and available on request via contact.

Reproduce it

Everything below runs on a stock machine with Homebrew (brew install tesseract tesseract-lang poppler, pip install pypdf pillow):

# 1. Get the source documents
curl -O https://www.boj.or.jp/mopo/outlook/gor2604a.pdf   # Japanese (pages 2, 10)
# Chinese: 2026 NDRC report (page 3) — see source link above

# 2. Simulate a scan: rasterize at 200 DPI, re-embed as image-only PDF
pdftoppm -f 2 -l 2 -r 200 -png gor2604a.pdf page
python3 -c "from PIL import Image; Image.open('page-02.png').convert('RGB').save('scan.pdf','PDF',resolution=200.0)"

# 3. Run the tools on the identical input
tesseract page-02.png out -l jpn                          # Tesseract 5.5.1
python3 -c "from pypdf import PdfReader; print(PdfReader('scan.pdf').pages[0].extract_text())"

base64 -i scan.pdf -o scan.b64                            # pdfToMarkdown
python3 -c "import json,base64; print(json.dumps({'input':{'pdf_base64':open('scan.b64').read()}}))" > payload.json
curl -X POST https://pdftomarkdown.dev/v1/convert \
  -H "Authorization: Bearer demo_public_key" \
  -H "Content-Type: application/json" -d @payload.json

Try it on your own documents

The demo key used above works for anyone — no signup, page 1 of any PDF. Guides: 日本語ページ · 中文页面 · API docs · CLI (npx pdftomarkdown 書類.pdf).