Font Recognizer

Identify a Cyrillic Font from an Image

2.0 10 / 10

Most font finders were trained on Latin only and guess badly on Cyrillic. This one was trained on Cyrillic letterforms too — upload a crop of Ukrainian, Russian or mixed text and get the closest families from the library.

Upload Text Sample

PNG, JPG, WEBP or BMP up to 8MB. Best results come from a tightly cropped word or short line.

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Drop text image here, click to upload or paste (Ctrl+V)

Single-line crop recommended for cleaner matches

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Select the text fragment to identify
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Recognizing
Matching against 31,000+ font families

Why Cyrillic is harder

Cyrillic has letterforms with no Latin counterpart — д, ж, ф, ц, щ, ъ — and many that look alike across typefaces. A recognizer trained on Latin samples has never seen these shapes, so it matches on the few shared letters (а, о, е, р) and ranks poorly. Our model was trained on rendered text in Latin and Cyrillic for every family that supports the script, so the distinctive Cyrillic glyphs contribute to the match instead of being ignored.

Ukrainian, Russian, Bulgarian, Serbian

The library carries thousands of families with Cyrillic support, including Ukrainian type foundries and Google Fonts’ Cyrillic subsets. Localized forms (Bulgarian and Serbian italic variants) are matched by shape, so a sample in any Cyrillic language works. Mixed text — a Latin brand name next to a Cyrillic tagline — works too.

Getting the best match

Crop tightly to one line of text, keep it horizontal, and prefer a sample with distinctive letters (ж, ф, щ, ю) over one made only of о and а. The OCR step reads the text automatically; correct it if a letter was misread — exact text improves ranking. Results link straight to the family page, where you can preview your own text in the font and download it.

Cyrillic font finder — questions

Yes — ґ, є, і, ї are part of the training samples for every family that supports them.

You still get the closest visual matches, which is usually enough to find a free alternative. Browse Cyrillic fonts by category on the Cyrillic fonts hub for more.

On our benchmark of Cyrillic samples the correct family is the first result about 4 times out of 10 and in the top five about 3 times out of 4 — noticeably better than Latin-only recognizers on the same set.

How the Font Recognizer Works

Identify fonts from images. Upload a text screenshot and find the closest matching fonts in the library.

Image-Based Matching

Upload a cropped screenshot of text and a trained neural network converts the letterforms into a compact visual fingerprint, then ranks every font in the library by similarity.

Library-Wide Search

The recognizer matches against 31,000+ font families, covering sans-serif, serif, display, handwriting, and monospace categories.

No Text Input Needed

You never have to type out the text in your image — the model matches letter shapes directly, with no OCR involved. It was trained on both Latin and Cyrillic, so Ukrainian text works as well as English.

Direct Font Preview

Click any match to jump to the full font profile. Preview all weights, check character coverage, and download the font — all without leaving the workflow.

Frequently Asked Questions

Accuracy depends on image quality and text clarity. Clean, high-contrast images of a single line of text produce the best results. The tool typically places the correct font in the top 5 matches for well-known typefaces.

Crop the image tightly around the text you want to identify. Use a horizontal, single-line sample with at least 4-5 characters. Avoid curved, distorted, or heavily stylized text, as these are harder to match.

The recognizer only matches against fonts in the Jinero library. If the exact font is not available, it will show the closest visual alternatives, which can still be useful for finding similar typefaces.

It can match some handwriting and script fonts, but results are less reliable than for standard text faces. Handwriting styles vary more between individual letterforms, making automated comparison harder.

Yes. The recognition model was trained and benchmarked on both Latin and Cyrillic letterforms, so a screenshot of Ukrainian text is matched with the same pipeline — and the same accuracy focus — as English.