Improving Arbitrary-shape Scene Text Detection with Visual-Relational Rectification and Contour Approximation

Keskeiset käsitteet

The core message of this paper is that by fusing visual and relational features of text segments, and using a novel shape approximation strategy, bottom-up methods can outperform state-of-the-art top-down approaches for arbitrary-shape scene text detection.

Tiivistelmä

The paper proposes an improved bottom-up approach for arbitrary-shape scene text detection that addresses the limitations of existing bottom-up methods. The key contributions are:

1. Utilizing the node classification ability of Graph Convolutional Networks (GCNs), in addition to their link prediction ability, to rectify text segments and suppress false positives/negatives. This is done by annotating text segments as Char Segments, Interval Segments, and Non-Text Segments in a weakly supervised manner.

2. Developing a visual-relational reasoning approach that fuses the visual features from the Feature Pyramid Network (FPN) with the relational features from the GCNs. This provides additional long-range dependency to guide the connectivity and integrity of the proposed text regions, further suppressing false detections.

3. Designing dense, overlapping text segments to better capture the "characterness" and "streamline" properties of text, and proposing a novel shape approximation strategy to group the rectified text segments without the error-prone route-finding process.

Experiments on curved text datasets like CTW1500 and Total-Text show that the proposed method outperforms state-of-the-art top-down and bottom-up approaches, demonstrating the effectiveness of the proposed techniques in revitalizing the strengths of bottom-up methods for arbitrary-shape scene text detection.

Tilastot

The proposed method outperforms state-of-the-art methods on CTW1500 dataset with an F-measure of 86.4%.
The proposed method outperforms state-of-the-art methods on Total-Text dataset with an F-measure of 87.6%.
The proposed method achieves comparable state-of-the-art results on ICDAR2015 dataset with an F-measure of 89.5%.
The proposed method equals the state-of-the-art result on MSRA-TD500 dataset with an F-measure of 87.0% and the highest recall rate of 83.8%.

Lainaukset

"The core message of this paper is that by fusing visual and relational features of text segments, and using a novel shape approximation strategy, bottom-up methods can outperform state-of-the-art top-down approaches for arbitrary-shape scene text detection."
"Experiments on curved text datasets like CTW1500 and Total-Text show that the proposed method outperforms state-of-the-art top-down and bottom-up approaches, demonstrating the effectiveness of the proposed techniques in revitalizing the strengths of bottom-up methods for arbitrary-shape scene text detection."

Tärkeimmät oivallukset

What's Wrong with the Bottom-up Methods in Arbitrary-shape Scene Text Detection

by Chengpei Xu,... klo arxiv.org 04-23-2024

https://arxiv.org/pdf/2108.01809.pdf

Syvällisempiä Kysymyksiä

How can the proposed visual-relational reasoning approach be extended to other computer vision tasks beyond text detection, such as object detection or instance segmentation

The proposed visual-relational reasoning approach can be extended to other computer vision tasks beyond text detection by adapting the concept of fusing visual and relational features to improve the accuracy and robustness of the models. For object detection, the visual-relational features can be used to enhance the understanding of spatial relationships between objects in an image. By incorporating relational reasoning into the feature extraction process, the model can better capture the context and interactions between different objects, leading to more accurate object localization and classification.
Similarly, in instance segmentation, the visual-relational features can help in better delineating the boundaries of individual instances within a scene. By considering the relationships between different parts of an object or instance, the model can improve its segmentation accuracy and handle complex scenarios where instances are closely intertwined or overlapping. This approach can enhance the performance of instance segmentation models by providing a more comprehensive understanding of the spatial layout and structure of objects in an image.
Overall, the visual-relational reasoning approach can be a valuable addition to various computer vision tasks, enabling models to leverage both visual features and relational information to make more informed decisions and improve overall performance.

What are the potential limitations of the weakly supervised text segment annotation approach, and how could it be further improved

The weakly supervised text segment annotation approach has some potential limitations that could be further improved. One limitation is the reliance on synthetic data for pre-training, which may not fully capture the complexity and variability of real-world text instances. This could lead to a domain gap between the synthetic data and real data, affecting the generalization ability of the model. To address this limitation, incorporating more diverse and representative real-world data during pre-training could help bridge the domain gap and improve the model's performance on real datasets.
Another limitation is the reliance on manual verification of the weakly supervised training results, which can be time-consuming and subjective. To improve this aspect, automated or semi-automated methods for verifying the correctness of the weakly supervised annotations could be implemented. This could involve leveraging additional validation techniques or incorporating feedback mechanisms to iteratively refine the annotations and ensure their accuracy.
Additionally, the weakly supervised approach may struggle with ambiguous or challenging cases where the distinction between different text segment types is not clear-cut. To address this, incorporating more sophisticated classification algorithms or introducing additional contextual information could help improve the accuracy and reliability of the text segment annotations.

What other applications or domains could benefit from the dense, overlapping text segment representation and the shape approximation strategy proposed in this work

The dense, overlapping text segment representation and the shape approximation strategy proposed in this work have potential applications in various domains beyond text detection.
One application could be in medical imaging, where the dense overlapping representation could be used to segment and analyze complex structures such as tumors or organs in medical scans. By densely representing the boundaries and shapes of different structures, the model could improve the accuracy of segmentation and assist in medical diagnosis and treatment planning.
In the field of autonomous driving, the shape approximation strategy could be applied to detect and track objects on the road, such as vehicles, pedestrians, and road signs. By approximating the contours of objects with dense overlapping segments, the model could better understand the spatial layout of the environment and make more informed decisions for navigation and obstacle avoidance.
Furthermore, in industrial automation, the dense representation and shape approximation could be utilized for quality control and defect detection in manufacturing processes. By accurately delineating the shapes and boundaries of products or components, the model could identify defects or anomalies with higher precision, improving the overall quality assurance processes in manufacturing.
Overall, the dense, overlapping text segment representation and shape approximation strategy have the potential to enhance various applications that require precise object detection, segmentation, and shape analysis in diverse domains.