  Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR)
have led to models with remarkable performance on both modern and historical
manuscripts in large benchmark datasets. Nonetheless, those models struggle to
obtain the same performance when applied to manuscripts with peculiar
characteristics, such as language, paper support, ink, and author handwriting.
This issue is very relevant for valuable but small collections of documents
preserved in historical archives, for which obtaining sufficient annotated
training data is costly or, in some cases, unfeasible. To overcome this
challenge, a possible solution is to pretrain HTR models on large datasets and
then fine-tune them on small single-author collections. In this paper, we take
into account large, real benchmark datasets and synthetic ones obtained with a
styled Handwritten Text Generation model. Through extensive experimental
analysis, also considering the amount of fine-tuning lines, we give a
quantitative indication of the most relevant characteristics of such data for
obtaining an HTR model able to effectively transcribe manuscripts in small
collections with as little as five real fine-tuning lines.
