If you are trying to detect text from document images / photos: tesseract is strongly focused mainly on the OCR part of the whole preprocessing(1). If you would like to get better results on that, you could use the wolf binarization tool (2) as an easy adaptive thresholding to remove shadows and uneven areas, which should improve your OCR results a lot on document photos!
Ah, the nostalgia. More than 10 years ago I wrote a local adaptive binarization tool to improve Tesseract's results, and the upstream still hasn't picked up the idea.
Many years ago there was specific OCR software (SpotlightPro / RasterDesk) for vectorize scanned technical drawings to CAD formats (with dimension lines, text labels, etc.); now there is Scan2CAD[0]. All them are proprietary software.
Sadly I can't find any open-source vectorizer & OCR for repair scanned technical drawings, and Tesseract has a lot of issues with rotated text labels specific to CAD.[1]
Image processing strongly depends on what image you wanna use. To find an "auto" approach, that works for every image is nearly impossible...
I once wrote a bookscanner app in Java (https://boofcv.org), where everything was done automatically (preprocessing, object detection / book extraction, skin detection / finger removal, deskewing, line-slope-correction and so on). It was very difficult to adjust the parameters, that at least most of the books looked good.
(1): https://towardsdatascience.com/pre-processing-in-ocr-fc231c6... (2): https://github.com/chriswolfvision/local_adaptive_binarizati...