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Isn't this an issue with the algorithm?

Can someone try and see how it would perform if you simply upscale the image using normal bicubic interpolation? And if it performs much better, I feel like that should be a preprocessing option to scale up the image since it seems to do so poorly on small resolutions.

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Tesseract's Github Readme actually recommends upscaling for better results:

https://github.com/naptha/tesseract.js#tesseractrecognizeima...

> Note: image should be be sufficiently high resolution. Often, the same image will get much better results if you upscale it before calling recognize.

Presumably, the result would be even better if you put a filter that did a path-tracing/auto-vectorization step first, upscaled by an arbitrary amount, and then rasterized the result. Analogue sources would have their traced paths distorted by such a process in a way that's lossier than just using the noisy analogue source—but for actually-digital-from-the-beginning images, it'd work perfectly.

This would be really cool! We would welcome a pull request and / or link to any downstream project working on this. In a similar vein, we've been considering adding the stroke width transform [0] as an optional preprocessing step.

[0] https://www.microsoft.com/en-us/research/publication/stroke-...

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