  Tashkeel, or Arabic Text Diacritization (ATD), greatly enhances the
comprehension of Arabic text by removing ambiguity and minimizing the risk of
misinterpretations caused by its absence. It plays a crucial role in improving
Arabic text processing, particularly in applications such as text-to-speech and
machine translation. This paper introduces a new approach to training ATD
models. First, we finetuned two transformers, encoder-only and encoder-decoder,
that were initialized from a pretrained character-based BERT. Then, we applied
the Noisy-Student approach to boost the performance of the best model. We
evaluated our models alongside 11 commercial and open-source models using two
manually labeled benchmark datasets: WikiNews and our CATT dataset. Our
findings show that our top model surpasses all evaluated models by relative
Diacritic Error Rates (DERs) of 30.83\% and 35.21\% on WikiNews and CATT,
respectively, achieving state-of-the-art in ATD. In addition, we show that our
model outperforms GPT-4-turbo on CATT dataset by a relative DER of 9.36\%. We
open-source our CATT models and benchmark dataset for the research
community\footnote{https://github.com/abjadai/catt}.
