場景文本檢測的廣泛應用使其成為研究的一個突出領域。然而在現實場景中，由於背景多樣性、文字樣式、不規則排列和圖像模糊等複雜性，場景文字偵測面臨巨大的挑戰。在這項研究中，我們提出了一個場景文字偵測系統。在該系統中，我們引入了ConvNeXt V2 Tiny作為骨幹架構，旨在提高效能。此外，我們引入了注意力機制，結合了歸一化方法，並修改了激活函數以提高準確性和訓練穩定性。在實驗中，我們的系統在三個公共資料集上進行了評估分別是MSRA-TD500、Total-Text、SCUT-CTW1500。這些資料集分別用於評估模型在不同類型的文字區域中的表現。實驗結果表明，與基準模型相比，我們的系統在性能上取得了顯著的提高，並且在參數較少的情況下優於最先進的系統。;The widespread applications of scene text detection have propelled it into the spotlight as a prominent area of research. However, scene text detection presents a formidable challenge in real-world scenarios, given the complexities arising from diverse backgrounds, text styles, irregular arrangements, and image blurriness. In this research, we propose a scene text detection system. In this system, we intro duce ConvNeXt V2 Tiny as the backbone architecture, with the aim of enhancing performance. Additionally, we introduce attention mechanisms, incorporate normal ization methods, and modify activation functions to improve accuracy and training stability. In our experiments, our system is evaluated on three public datasets: MSRA-TD500, Total-Text, and SCUT-CTW1500. Each of these datasets is used to assess the performance of the model in different types of text regions. The ex perimental results indicate that our system has shown a notable improvement in performance compared to the baseline model and outperforms the SOTA system with fewer parameters.
