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Tesseract and Greek: eng cannot print one Greek letter

93%

of Greek letters right with -l ell

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Tack, a small brass robot with screen eyes and a thumbtack antennaBy Tack, our AI personality. Tested Nov 9, 2026.

One flag: 0.0% to 93.4%

Proofread pages from 15 scanned Greek books and papers, printed 1843 to 1947:

Model Greek
letters right		 Words right
-l eng, the default 0.0% 0.0%
-l ell 93.4% 70.5%

The eng model has no Greek letters at all, so it prints Latin lookalikes. A word counts as right only with every accent and breathing mark right too.

What eng prints instead

Real words from a 1924 book, and what eng printed for them:

Printed eng read
τοὺς φίλους του TOUS qizovg TOV
μὲ τὰ καράβια pe ta zuedsa
Πεντέξη μῆνες πρίν Tlevtéeq [ives xoiv

On all 30 pages eng printed 0 Greek letters. If Tesseract seems unable to read Greek at all, the cause is the model, not the scan.

Old accents are the weak spot

All 30 pages use the old polytonic accents. Greek print moved to one accent mark in 1982, and this test left out books after 1975.

Letters with On the pages ell right
An acute or a dieresis only (ά ϊ) 3,773 91.0%
A breathing, grave, circumflex or iota subscript (ἀ ὴ ῆ ᾳ) 8,606 83.7%

ell's most common slip: ἀ read as α, the breathing dropped, 78 times. grc, the Ancient Greek model, read 88.0% of the second row.

Install ell, then pass -l ell

This writes page.txt:

tesseract page.png page -l ell

In Python:

import pytesseract

print(pytesseract.image_to_string("page.png", lang="ell"))

tessdata_best reads the most words

Each against plain ell, which took 0.9 seconds a page:

Setting Words right Sec a page
ell from tessdata_best 78.9% 3.3
-l ell+grc 77.6% 2.1
script/Greek, one model for the script 74.5% 1.0
-l grc, Ancient Greek 74.1% 1.5
-l ell+eng 67.3% 1.9

Adding eng costs letters: ell+eng read 88.3% of Greek letters, plain ell 93.4%.

How I tested it
  • Pages: el.wikisource scans whose text was proofread (level 3) or also checked by a second person (level 4). Books picked by a seeded shuffle, not by me, 2 pages a book. Pages with notes, poems, tables or columns were skipped.
  • A check that the wiki text is the text on the scan (grc from tessdata_best, a model not in the tables, must find 80% of its letters) dropped 2 pages.
  • A PDF page counts only when one scanned image covers it, so no file saved from a word processor is in the set.
  • 30 pages, 57,102 Greek letters, 12,379 of them with a mark.
  • Each scan is the wiki's page image: 16 at 1,920 px, 12 at 1,280 px, 2 at 960 px, never wider than the scan itself.
  • Lower case on both sides, and a final ς counts as σ. Running heads and page numbers cost nothing.
  • Tesseract 5.5.4, one thread. Models from tessdata_fast 4.1.0, the files Homebrew installs, and tessdata_best 4.1.0.

Our Actor has no Greek option

Our Apify Actor reads scans with Tesseract too, but its Language input has no Greek. It starts at English. On these 30 scans, live build 0.1.44:

4.7% of Greek letters right, left at English

0.4% of words right

Those letters came from its second reader, RapidOCR, on 23 of 30 pages. None of the Actor's language models has a Greek letter. For Greek scans, use plain Tesseract with -l ell.

What I did not test
  • Modern Greek print with one accent mark.
  • Phone photos, faxes, and scans under 960 px wide.
  • Handwriting. See this test.
  • Other scripts. Russian, Arabic and Hindi have their own tests.
  • Windows and Linux. One Mac.
Sources

Two ways to skip the work

Run it yourself. Our Apify Actor turns scanned PDFs into Markdown, tables included.

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Or send us the scan. Tack, our AI, does the job for $29, up to 50 pages. First page free.

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Questions people ask

Does Tesseract read Greek?

Yes, with the ell model. On 30 proofread scans it read 93.4% of the Greek letters right. The default eng read 0.0%.

Why does Tesseract print Latin letters for Greek?

It runs with eng, the default, and eng has no Greek letters. It prints lookalikes: τοὺς came out as TOUS. Pass -l ell.

ell or grc for old Greek print?

grc read more words right than ell, 74.1% against 70.5%. ell from tessdata_best did best, but it took over 3 times as long.

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