Accurate Arbitrary-shape Scene Text Detection using Deep Morphology Regularization

Conceitos essenciais

The proposed MorphText approach effectively embeds deep morphology to regularize text segments, addressing the issues of false text segment detections and missing linkages between text segments in bottom-up arbitrary-shape scene text detection methods.

Resumo

The paper proposes a novel arbitrary-shape scene text detection approach called "MorphText" that leverages deep morphology to regularize text segments and alleviate the false detection and missing linkage problems of existing bottom-up methods.

Key highlights:

• Two deep morphological modules are designed:
  1. Deep Morphological Opening (DMOP) module to remove false text segment detections
  2. Deep Morphological Closing (DMCL) module to determine the linkage between text segments
• The DMOP module utilizes trainable structure elements and residual connections to regularize the text segment and center line detection results.
• The DMCL module processes the resultant text segments, determining the connections between them based on their morphology.
• The overall network can be trained in an end-to-end manner, replacing the error-prone post-processing steps in bottom-up methods.
• Extensive experiments on benchmark datasets show that the proposed MorphText outperforms state-of-the-art arbitrary-shape text detection approaches.

Estatísticas

The proposed MorphText approach outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017).
Ablation studies show that the DMOP and DMCL modules are effective in removing false detections and addressing the missing linkage problem, respectively.
The selection of the structure element size and the number of erosion/dilation layers in DMOP and DMCL impacts the performance, with 2×2 and 3×3 kernels achieving the best results.
The residual connection in DMOP and DMCL helps to alleviate the over-correction problem.

Citações

"Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments."
"Is there a method that can remove false detections while simultaneously addressing the linkage problem of bottom-up approaches?"

Principais Insights Extraídos De

MorphText: Deep Morphology Regularized Arbitrary-shape Scene Text Detection

by Chengpei Xu,... às arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17151.pdf

Perguntas Mais Profundas

How can the proposed deep morphological modules be further improved or extended to handle more complex text patterns and layouts

The proposed deep morphological modules can be further improved or extended to handle more complex text patterns and layouts by incorporating advanced techniques and strategies. One way to enhance the modules is to introduce adaptive or dynamic structure elements that can adjust their shape and size based on the characteristics of the text instances. This adaptability can help in capturing intricate patterns and irregular shapes more effectively. Additionally, integrating attention mechanisms or contextual information into the deep morphology modules can improve their ability to focus on relevant text features and contextually connect different text segments. Moreover, exploring hierarchical deep morphology networks with multiple levels of abstraction can enable the modules to analyze text patterns at different scales and complexities, leading to more robust and accurate text detection results.

What are the potential limitations of the deep morphology-based approach compared to other text detection techniques, and how can they be addressed

The deep morphology-based approach, while effective in regularizing text segments and addressing false detections, may have some limitations compared to other text detection techniques. One potential limitation is the computational complexity of deep morphological operations, which can be higher than traditional methods like CNNs. This increased computational cost may impact the real-time performance of the system, especially when processing large volumes of text data. To address this limitation, optimizing the implementation of deep morphology algorithms and leveraging parallel processing techniques can help improve efficiency. Another limitation could be the interpretability of the deep morphology modules, as the learned structure elements and operations may not be easily interpretable by humans. To overcome this, visualization techniques and explainable AI methods can be employed to provide insights into how the deep morphology modules make decisions and process text data.

How can the proposed MorphText framework be adapted or combined with other computer vision tasks beyond text detection, such as object detection or instance segmentation

The proposed MorphText framework can be adapted or combined with other computer vision tasks beyond text detection, such as object detection or instance segmentation, by leveraging the strengths of deep morphology in capturing patterns and structures. For object detection, the deep morphology modules can be integrated into the detection pipeline to enhance the localization and segmentation of objects with complex shapes and textures. By incorporating deep morphology regularization and linking modules, the framework can improve the accuracy and robustness of object detection systems, especially for objects with irregular shapes or occlusions. Similarly, in instance segmentation tasks, MorphText can be used to refine the segmentation masks and connect fragmented instances, leading to more precise and coherent segmentation results. By extending the MorphText framework to these tasks, it can offer a comprehensive solution for various computer vision applications requiring accurate and detailed analysis of visual data.