Best practices to fine-tune a model?

[Profs Stars]

I have a few questions regarding the fine-tuning process.
I'm building an app that is able to recognize data from the following documents:

- ID Card
- Driving license
- Passport
- Receipts

All of them have different fonts (especially receipts) and it is hard to match exactly the same font and I will have to train the model on a lot of similar fonts.

So my questions are:

1. Should I train a separate model for each of the document types for better performance and accuracy or it is fine to train a single `eng` model on a bunch of fonts that are similar to the fonts that are being used on this type of documents?

2. How many pages of training data should I generate per font? By default, I think `tesstrain.sh` generates around 4k pages.
Maybe any suggestions on how I can generate training data that is closest to real input data

3. How many iterations should be used?

For example, if I'm using some font that has a high error rate and I want to target `98% - 99%` accuracy rate.

As well maybe some of you had experience working with this type of documents and maybe you know some common fonts that are being used for these documents?

I know that MRZ in passport and id cards is using `OCR-B` font, but what about the rest of the document?

Thanks in advance!