Which Low-Resource Languages Continue to Challenge Tesseract?

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Alro wilde

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May 28, 2026, 2:44:52 PM (2 days ago) May 28
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Hi everyone,

I'm looking for input on which emerging market languages currently have the most urgent need for better OCR support.

Many low-resource languages still suffer from poor or missing trained models in Tesseract-OCR and PaddleOCR, mainly because collecting enough high-quality real data is extremely time-consuming and expensive.

I’ve developed a synthetic data generation tool (Synthetic Engine) specifically for this problem. It can create large volumes of realistic training samples for scripts and languages where real labeled data is scarce. This allows us to quickly bootstrap and train new language models.

I’d like to collect feedback from the community:

  • Which languages or scripts in emerging markets are you finding most difficult to support right now?
  • Where is the current support in Tesseract-OCR and PaddleOCR clearly insufficient?

I’m happy to use my tool to help generate synthetic data and attempt to build a new model for the languages that need it most. If you’re interested, I can also share sample synthetic data or run small experiments.

Looking forward to your thoughts!

Best regards, Alro Wilde

Dmitry Yatcenko

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May 29, 2026, 9:50:17 AM (yesterday) May 29
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I use Tesseract in a program for translating and redesigning card games. Often, the problem isn't the language, but the grotesque fonts on the cards. Furthermore, I have a font file, but without training the model, I can't force the OCR to recognize a specific font. I'd like a simple and user-friendly solution—one that would allow me to create a model for a specific font file in two clicks, optionally linking it to a specific language (Russian, English, Spanish). While it's interesting, it would be impossible to recognize icons by replacing them with macros like [gun],[sword],[hearth]...

четверг, 28 мая 2026 г. в 21:44:52 UTC+3, alro...@gmail.com:

Alro wilde

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May 29, 2026, 12:42:07 PM (23 hours ago) May 29
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It seems that you can use the template match to solve this problem. If the font or the name of the card are big enough.

And you can take the Yolo into your technical stack, In my experience, the number of cards are enumerable. it maybe a classification task.
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