The longer you train HTR models yourself, the more you will be interested in the possibility of combining models. For example, you may want to combine several special models for individual writers or models that are specialized in particular fonts or languages.
To achieve a combination of models there are different possibilities. Here I would like to introduce a technique that works in my experience especially well for very large generic models – the “Model Booster“.
You start a base model training and use a powerful, foreign HTR model as base model and your own ground truth as train set. But before you start, two recommendations:
a) take a close look at the characteristics of the base model you are using (for how long is it trained, for which font style and which language?) – they have to match those of your own material as much as possible.
b) if possible try to predict the performance of the base model on your own material and then choose the base model with the best performance. Such a prediction can be made quite easily using the Sample Compare function. Another possibility is to test the basemodel with the Andvanced Compare on your own test set.