Scene Text Detection Based on Attention ConvNeXt

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姓名

張育陞(Yu-Sheng Chang) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

(Scene Text Detection Based on Attention ConvNeXt)

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至系統瀏覽論文 (2026-1-25以後開放)

摘要(中)

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。

摘要(英)

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 introduce ConvNeXt V2 Tiny as the backbone architecture, with the aim of enhancing performance. Additionally, we introduce attention mechanisms, incorporate normalization 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 experimental 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.

關鍵字(中)

★ 場景文字檢測
★ 深度學習
★ 注意力機制

關鍵字(英)

★ Scene Text Detection
★ deep learning
★ attention

論文目次

1 Introduction 1
2 Related Work 3
2.1 Backbone Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.1 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . 3
2.1.2 Convolutional Neural Networks with Attention Mechanisms . . . . 3
2.1.3 Vision Transformers . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Text Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2.1 Regression-based Scene Text Detection . . . . . . . . . . . . . . . . 4
2.2.2 Connected-based Scene Text Detection . . . . . . . . . . . . . . . . 5
2.2.3 Segmentation-based Scene Text Detection . . . . . . . . . . . . . . 5
2.2.4 Transformer-based Scene Text Detection . . . . . . . . . . . . . . . 6
3 Preliminary 7
3.1 Sigmoid-weighted Linear Unit . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Data Augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 ConvNeXt V2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4 Weight Standardization and Batch Channel Normalization . . . . . . . . . 9
3.5 Convolutional Block Attention Module . . . . . . . . . . . . . . . . . . . . 10
3.6 Asymmetric Convolutional Network . . . . . . . . . . . . . . . . . . . . . . 11
3.7 Faster Arbitrarily-Shaped Text Detector . . . . . . . . . . . . . . . . . . . 12
4 Design 14
4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 Proposed System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3.1 Data Augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3.2.1 Backbone Part . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3.2.2 Detection Part . . . . . . . . . . . . . . . . . . . . . . . . 19
5 Performance 21
5.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.4 Experimental Results and Analysis . . . . . . . . . . . . . . . . . . . . . . 24
5.4.1 Scenes with long text lines . . . . . . . . . . . . . . . . . . . . . . . 24
5.4.2 Scenes with curved text lines . . . . . . . . . . . . . . . . . . . . . . 25
5.4.3 Scenes with curved text and small text lines . . . . . . . . . . . . . 26
5.5 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6 Conclusions 30
Reference 31

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指導教授

孫敏德(Min-Te Sun)

審核日期

2024-1-26

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