You can train Tesseract to recognize Handwriting[1], but the first and most important step would be the preprocessing of your documents. I would recommend to st

You can train Tesseract to recognize Handwriting[1], but the first and most important step would be the preprocessing of your documents. I would recommend to start with a local adaptive thresholding algorithm[2] like Sauvola for binarization. The preprocessing steps would be[3] 1) Binarization 2) Skew Correction 3) Noise Removal 4) Thinning and Skeletonization Probably you are facing "Sütterlin"[4], which differs quite a bit from modern german handwriting. In your case (only 200 pages) it might be easier to use template matching[5] to identify similar characters and just "transliterate" matches into modern printed letters (like an overlay over the original text). This way you would have a quick solution while still being accurate enough to just read it. [1]: https://tesseract-ocr.github.io/tessdoc/#training-for-tesseract-5 https://tesseract-ocr.github.io/tessdoc/#training-for-tesser... [2]: https://brandonmpetty.github.io/Doxa/WebAssembly/ https://brandonmpetty.github.io/Doxa/WebAssembly/ [3]: https://towardsdatascience.com/pre-processing-in-ocr-fc231c6035a7 https://towardsdatascience.com/pre-processing-in-ocr-fc231c6... [4]: https://de.wikipedia.org/wiki/S%C3%BCtterlinschrift https://de.wikipedia.org/wiki/S%C3%BCtterlinschrift [5]: https://docs.opencv.org/3.4/d4/dc6/tutorial_py_template_matching.html https://docs.opencv.org/3.4/d4/dc6/tutorial_py_template_matc...