<p>Lexicon-free Online Arabic Handwriting Recognition (OAHR) remains a formidable challenge due to the script’s highly cursive nature and the critical absence of word-level constraints. Capturing the complex long-range temporal dependencies inherent in online stroke data is key to improving recognition accuracy. While Recurrent Neural Networks (RNNs) often fall short, Multi-Head Attention (MHA) mechanisms have emerged as superior context aggregators, offering both enhanced dependency capture and efficient parallel computation. Motivated by this, we propose an enhanced BGRU-MHA hybrid network for lexicon-free OAHR. This architecture integrates stacked BGRUs with a MHA module to construct robust, context-aware feature representations followed by a Connectionist Temporal Classification (CTC) output layer for character sequence generation. Spatial and temporal features are first extracted from the preprocessed handwriting input, then refined through the BGRU-MHA hybrid layers, enabling the model to capture both local and global dependencies. The proposed approach was evaluated on ADAB and Online-KHATT benchmark datasets. The results demonstrate that our method establishes a new state of the art. Specifically, the model achieved a Character Error Rate (CER) of 3.86% and 6.79%, and a Word Error Rate (WER) of 11.98% and 20.36% on ADAB and Online-KHATT, respectively. These results confirm that integrating MHA with BGRU significantly improves recognition accuracy, validating the effectiveness of the proposed lexicon-free framework.</p>