Ah wait, you said it might not be easily scriptable, so presumably not easily exportable as a graph either?

Have a look at exporting to ONNX and using ONNX Runtime or another runtime which supports that format: https://pytorch.org/docs/stable/onnx.html

To add to this, Tesseract's text recognition of identified lines of text uses a modern approach involving LSTM neural networks, but the text detection process which comes before this uses classical/heuristic (ie. non-ML) approaches which work well on clean-ish document images, but can struggle with photos of documents that have uneven lighting conditions and spotting text in a photo (eg. numberplates in a city scene).

I maintain a JavaScript build of Tesseract with an online demo that you can try with different images: https://robertknight.github.io/tesseract-wasm/