  PyLaia is one of the most popular open-source software for Automatic Text
Recognition (ATR), delivering strong performance in terms of speed and
accuracy. In this paper, we outline our recent contributions to the PyLaia
library, focusing on the incorporation of reliable confidence scores and the
integration of statistical language modeling during decoding. Our
implementation provides an easy way to combine PyLaia with n-grams language
models at different levels. One of the highlights of this work is that language
models are completely auto-tuned: they can be built and used easily without any
expert knowledge, and without requiring any additional data. To demonstrate the
significance of our contribution, we evaluate PyLaia's performance on twelve
datasets, both with and without language modelling. The results show that
decoding with small language models improves the Word Error Rate by 13% and the
Character Error Rate by 12% in average. Additionally, we conduct an analysis of
confidence scores and highlight the importance of calibration techniques. Our
implementation is publicly available in the official PyLaia repository at
https://gitlab.teklia.com/atr/pylaia, and twelve open-source models are
released on Hugging Face.
