Fourier Contour Embedding for Arbitrary-Shaped Text Detection (2104.10442v2)

Abstract: One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. However, the mask representation might lead to expensive post-processing, while the point sequence one may have limited capability to model texts with highly-curved shapes. To tackle these problems, we model text instances in the Fourier domain and propose one novel Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours as compact signatures. We further construct FCENet with a backbone, feature pyramid networks (FPN) and a simple post-processing with the Inverse Fourier Transformation (IFT) and Non-Maximum Suppression (NMS). Different from previous methods, FCENet first predicts compact Fourier signatures of text instances, and then reconstructs text contours via IFT and NMS during test. Extensive experiments demonstrate that FCE is accurate and robust to fit contours of scene texts even with highly-curved shapes, and also validate the effectiveness and the good generalization of FCENet for arbitrary-shaped text detection. Furthermore, experimental results show that our FCENet is superior to the state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on challenging highly-curved text subset.

Essay on "Fourier Contour Embedding for Arbitrary-Shaped Text Detection"

The paper "Fourier Contour Embedding for Arbitrary-Shaped Text Detection" by Yiqin Zhu et al. presents a novel approach aimed at enhancing the detection of arbitrary-shaped text in images, a challenging task in the domain of scene text detection. The authors introduce a method termed Fourier Contour Embedding (FCE), which leverages the Fourier domain to represent text contours with compact and flexible Fourier signature vectors.

Summary of Contributions

The primary contribution of the paper is the Fourier Contour Embedding (FCE) method, which models text contours using the Fourier transformation. This approach is particularly beneficial in dealing with the geometric variability of text shapes, especially those with highly curved contours. The authors argue that existing methods that utilize masks or contour point sequences in Cartesian or polar coordinates often face limitations such as computationally expensive post-processing or inadequate representation capability for complex shapes.

In the FCE architecture, the text contours are first resampled to generate a fixed number of contour points, ensuring consistency across different datasets. These points are then transformed into Fourier signature vectors using the Fourier transformation. The proposal posits notable advantages: flexibility in fitting any closed contour, compactness by representing contours with fewer parameters, and simplicity owing to the straightforward conversion process involving Fourier and Inverse Fourier Transformations.

The second significant contribution is the text detection framework named FCENet, built upon the proposed FCE method. FCENet comprises a backbone with a Feature Pyramid Network (FPN) and employs two branches dedicated to classification and contour regression. The classification branch identifies text regions and text center regions. Meanwhile, the regression branch predicts the Fourier signature vectors, which are subsequently used to reconstruct the text contours via Inverse Fourier Transformation (IFT). This novel approach considerably minimizes the need for complex post-processing typically seen in methods employing spatial domain representations.

Results and Implications

The paper thoroughly validates the effectiveness of the FCENet architecture across several benchmarks, namely CTW1500 and Total-Text datasets. The results reveal superior performance of FCENet over state-of-the-art methods, especially on subsets involving highly curved texts, showcasing the robustness and adaptability of the Fourier-based approach. Significant numerical results indicate FCENet's precision and F-measure consistently surpass those of existing methods, underscoring the method's capability to handle challenging text shapes effectively.

From a practical perspective, the reduced complexity and enhanced flexibility make the FCE method advantageous for applications requiring real-time text detection, such as augmented reality or autonomous driving systems. Theoretically, this work could be foundational for future research exploring the use of Fourier transformations for shape representation beyond text detection, potentially influencing computer vision tasks that involve complex shape modeling.

Future Directions

Future research could extend upon the current work by exploring more efficient training paradigms that further enhance generalization, especially in constrained data scenarios. Additionally, applying the Fourier Contour Embedding framework to a broader set of shape-based detection tasks could verify its versatility beyond the field of text detection. Exploring the integration of FCE with other neural network architectures might reveal new avenues for performance improvements.

The paper by Zhu et al. marks a significant step forward in the endeavor of accurately detecting arbitrary-shaped texts. The Fourier Contour Embedding strategy offers an insightful paradigm shift, emphasizing the potential for Fourier-based techniques in advancing complex geometric modeling in computer vision.