Recent research in script-specific text recognition has achieved remarkable performance on scene text images. However, the challenges of multilingual text in real-world images have not been effectively addressed, thereby limiting the practical application. For multilingual text recognition, the prevailing single-head scheme that uses the same recognition head for all scripts suffers from the issues of enormous alphabet size and linguistic knowledge discrepancies. Meanwhile, the multi-head scheme, which assigns distinct heads for individual scripts, encounters error accumulation within the two-step inference process. In this paper, we explore the influence of script attribute information within existing methods on achieving accurate recognition. Correspondingly, we propose a script-aware recognition network encompassing script identification and text recognition, which leverages script information to make character features more discriminative in text recognition. In particular, we design the script-aware module to utilize script information at the local level, global level, and their combination, respectively. Furthermore, our method is extended to the multi-head scheme, utilizing mutual assistance between script and character features to improve the performance in both script identification and text recognition tasks. Extensive experiments demonstrate that our method obtains superior results in multilingual text recognition, and reveal the importance of script information in alleviating the issues of single-head and multi-head recognition scheme.