Scene Text Detection Based on Two-Branch Feature Extraction

Ibrayim, Mayire; Li, Yuan; Hamdulla, Askar

doi:10.3390/s22166262

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…The global feature branch captures global features by adopting adaptive global average pooling and depthwise separable convolution and strengthens the focus on larger targets with a global distribution. On the other hand, local features capture specific details through depthwise separable convolution and highlight the characteristics of smaller targets [ 35 ]. Compared to SENet [ 36 ], which only employs a global attention mechanism, our module integrates text feature information of different scales on the channel dimension, thus mitigating the issues caused by text scale variations and enhancing the detection capability for small targets.…”

Section: Methodsmentioning

confidence: 99%

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion

Li,

Wang,

Huang

et al. 2024

Sensors

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Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds. To address the challenge of detecting diverse texts in natural scenes, this paper proposes a multi-scale natural scene text detection method based on attention feature extraction and cascaded feature fusion. This method combines global and local attention through an improved attention feature fusion module (DSAF) to capture text features of different scales, enhancing the network’s perception of text regions and improving its feature extraction capabilities. Simultaneously, an improved cascaded feature fusion module (PFFM) is used to fully integrate the extracted feature maps, expanding the receptive field of features and enriching the expressive ability of the feature maps. Finally, to address the cascaded feature maps, a lightweight subspace attention module (SAM) is introduced to partition the concatenated feature maps into several sub-space feature maps, facilitating spatial information interaction among features of different scales. In this paper, comparative experiments are conducted on the ICDAR2015, Total-Text, and MSRA-TD500 datasets, and comparisons are made with some existing scene text detection methods. The results show that the proposed method achieves good performance in terms of accuracy, recall, and F-score, thus verifying its effectiveness and practicality.

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Section: Methodsmentioning

confidence: 99%

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion

Li,

Wang,

Huang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…After the CNN were used to extract features, the performance of the STD model began to depend on the design of special components, like Region Proposal Network (RPN), Feature Pyramid Network (FPN) [13,14], anchors, and other factors [15,16]. These algorithms required a lot of prior knowledge and complex post-processing steps.…”

Section: Related Workmentioning

confidence: 99%

CA-STD: Scene Text Detection in Arbitrary Shape Based on Conditional Attention

Song

et al. 2022

Information

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Scene Text Detection (STD) is critical for obtaining textual information from natural scenes, serving for automated driving and security surveillance. However, existing text detection methods fall short when dealing with the variation in text curvatures, orientations, and aspect ratios in complex backgrounds. To meet the challenge, we propose a method called CA-STD to detect arbitrarily shaped text against a complicated background. Firstly, a Feature Refinement Module (FRM) is proposed to enhance feature representation. Additionally, the conditional attention mechanism is proposed not only to decouple the spatial and textual information from scene text images, but also to model the relationship among different feature vectors. Finally, the Contour Information Aggregation (CIA) is presented to enrich the feature representation of text contours by considering circular topology and semantic information simultaneously to obtain the detection curves with arbitrary shapes. The proposed CA-STD method is evaluated on different datasets with extensive experiments. On the one hand, the CA-STD outperforms state-of-the-art methods and achieves 82.9 in precision on the dataset of TotalText. On the other hand, the method has better performance than state-of-the-art methods and achieves the F1 score of 83.8 on the dataset of CTW-1500. The quantitative and qualitative analysis proves that the CA-STD can detect variably shaped scene text effectively.

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“…The object detection algorithm is adapted to the text detection task by migrating and improving it. Although such methods are fast in detection and can avoid the accumulation of multi-stage mistakes, most existing regression-based methods cannot accurately and effectively solve the text detection problem due to the limitations of the text expression form (axial rectangle, rotated rectangle, or quadrilateral); especially, there are limitations in detecting long text, curved text or arbitrarily shaped text [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Scene Text Detection on Dual Feature Fusion

Silamu

Wang

et al. 2022

Sensors

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The segmentation-based scene text detection algorithm has advantages in scene text detection scenarios with arbitrary shape and extreme aspect ratio, depending on its pixel-level description and fine post-processing. However, the insufficient use of semantic and spatial information in the network limits the classification and positioning capabilities of the network. Existing scene text detection methods have the problem of losing important feature information in the process of extracting features from each network layer. To solve this problem, the Attention-based Dual Feature Fusion Model (ADFM) is proposed. The Bi-directional Feature Fusion Pyramid Module (BFM) first adds stronger semantic information to the higher-resolution feature maps through a top-down process and then reduces the aliasing effects generated by the previous process through a bottom-up process to enhance the representation of multi-scale text semantic information. Meanwhile, a position-sensitive Spatial Attention Module (SAM) is introduced in the intermediate process of two-stage feature fusion. It focuses on the one feature map with the highest resolution and strongest semantic features generated in the top-down process and weighs the spatial position weight by the relevance of text features, thus improving the sensitivity of the text detection network to text regions. The effectiveness of each module of ADFM was verified by ablation experiments and the model was compared with recent scene text detection methods on several publicly available datasets.

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Scene Text Detection Based on Two-Branch Feature Extraction

Cited by 7 publications

References 36 publications

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion

CA-STD: Scene Text Detection in Arbitrary Shape Based on Conditional Attention

Attention-Based Scene Text Detection on Dual Feature Fusion

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