Efficient and Robust Arbitrary-Shaped Text Detection in Low-Light Environments

Conceitos Básicos

A one-stage approach for localizing arbitrary-shaped text in low-light conditions that effectively utilizes spatial constraints to guide the training process and captures the intrinsic topological and streamline features of text.

Resumo

The paper proposes a novel one-stage approach for localizing arbitrary-shaped text in low-light environments. The key contributions are:

1. A spatial-constrained learning module (SCM) is introduced during the training stage to guide the text detector in preserving textual spatial features amidst feature map resizing, minimizing the loss of spatial information under low-light degradations.

2. A dynamic snake feature pyramid network (DSF) is designed to capture the intricate local topological features inherent in textual elements, leveraging both dynamic snake convolution and regular convolution.

3. A bottom-up text shaping method with rotated rectangular accumulation (TSR) is employed to enhance the expression of text's streamlined topology, tolerating errors and requiring less intact text feature maps.

4. A new low-light arbitrary-shaped text dataset (LATeD) is curated, featuring 13,923 multilingual and arbitrary-shaped texts across diverse low-light scenes, bridging the existing domain gap.

The proposed method achieves state-of-the-art results on the LATeD dataset and exhibits comparable performance on standard normal-light datasets, demonstrating its effectiveness in both low-light and normal-light text detection.

Estatísticas

"Localizing text in low-light environments is challenging due to visual degradations."
"Insufficient lighting leads to visual degradations such as blurred details, reduced brightness and contrast, and distorted color representation, making it difficult for both humans and text detectors to locate text."
"Our method achieves state-of-the-art results on the low-light text detection dataset LATeD and exhibits comparable performance on standard normal-light datasets."

Citações

"For the first time, we propose a one-stage pipeline for low-light arbitrary-shaped text detection that effectively utilizes spatial constraint to adeptly guide the training process."
"We devise a novel method concentrating on the extraction of topological distribution features and the modeling of streamline characteristics to shape low-light text contours effectively."
"We curate the first low-light arbitrary-shape text dataset (LATeD) featuring 13,923 multilingual and arbitrary shape texts across diverse low-light scenes, effectively bridging the existing domain gap."

Principais Insights Extraídos De

Seeing Text in the Dark: Algorithm and Benchmark

by Chengpei Xu,... às arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08965.pdf

Perguntas Mais Profundas

How can the proposed spatial-constrained learning module be extended to other computer vision tasks beyond text detection that involve spatial information preservation

The proposed spatial-constrained learning module can be extended to other computer vision tasks beyond text detection by adapting the constraints to suit the specific requirements of those tasks. For instance, in object detection tasks, the spatial reconstruction constraint can be modified to focus on preserving the spatial relationships between object parts or components. This would involve creating masks for different object parts and ensuring that the network learns to reconstruct these spatial relationships accurately during training. Additionally, the spatial semantic constraint can be adjusted to emphasize the contextual information relevant to the specific objects being detected. By incorporating object-specific semantic constraints, the network can better understand the context in which different objects appear in the scene, leading to more accurate detection results.

What are the potential limitations of the bottom-up text shaping approach, and how could it be further improved to handle more complex text structures or occlusions

The bottom-up text shaping approach, while effective, may have limitations when dealing with more complex text structures or occlusions. One potential limitation is the handling of overlapping or intersecting text components, which can lead to inaccuracies in the final text contours. To address this limitation, the approach could be further improved by incorporating advanced algorithms for text segmentation and grouping. By implementing algorithms that can accurately separate overlapping text components and resolve occlusions, the model can better capture the intricate details of complex text structures. Additionally, integrating attention mechanisms or graph-based approaches to capture long-range dependencies between text components could enhance the model's ability to handle complex text layouts and structures.

Given the advancements in low-light image enhancement techniques, how could the proposed method be integrated with such techniques to achieve even better performance in low-light text detection

To leverage the advancements in low-light image enhancement techniques, the proposed method can be integrated by incorporating the enhanced images as input during the training phase. By pre-processing the low-light images with state-of-the-art enhancement techniques before feeding them into the network, the model can benefit from improved image quality and visibility. This integration can help the model focus on learning the intrinsic features of text in low-light conditions rather than struggling with image visibility issues. Furthermore, the enhanced images can provide a clearer representation of text details, enabling the model to make more accurate predictions. By combining the strengths of both the low-light text detection method and advanced enhancement techniques, the overall performance in low-light scenarios can be significantly enhanced.