  Automatic diacritization of Arabic text involves adding diacritical marks
(diacritics) to the text. This task poses a significant challenge with
noteworthy implications for computational processing and comprehension. In this
paper, we introduce PTCAD (Pre-FineTuned Token Classification for Arabic
Diacritization, a novel two-phase approach for the Arabic Text Diacritization
task. PTCAD comprises a pre-finetuning phase and a finetuning phase, treating
Arabic Text Diacritization as a token classification task for pre-trained
models. The effectiveness of PTCAD is demonstrated through evaluations on two
benchmark datasets derived from the Tashkeela dataset, where it achieves
state-of-the-art results, including a 20\% reduction in Word Error Rate (WER)
compared to existing benchmarks and superior performance over GPT-4 in ATD
tasks.
