arXiv:2404.17151v1 Announce Type: new
Abstract: 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. Targeting these two problems, we propose a novel approach, named “MorphText”, to capture the regularity of texts by embedding deep morphology for arbitrary-shape text detection. Towards this end, two deep morphological modules are designed to regularize text segments and determine the linkage between them. First, a Deep Morphological Opening (DMOP) module is constructed to remove false text segment detections generated in the feature extraction process. Then, a Deep Morphological Closing (DMCL) module is proposed to allow text instances of various shapes to stretch their morphology along their most significant orientation while deriving their connections. Extensive experiments conducted on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017) demonstrate that our proposed MorphText outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches.
Analysis: Novel Approach for Arbitrary-Shape Text Detection
Text detection in images is a challenging task, especially when dealing with texts of arbitrary shapes. In this article, the authors present a novel approach called “MorphText” to address two major issues in bottom-up text detection methods: the accumulation of false text segment detections and the difficulty of building reliable connections between text segments.
The Role of Deep Morphology in Text Detection
MorphText tackles these problems by leveraging deep morphology. The authors propose two deep morphological modules: Deep Morphological Opening (DMOP) and Deep Morphological Closing (DMCL).
The DMOP module plays a crucial role in removing false text segment detections that occur during the feature extraction process. By applying deep morphology techniques, the module is able to identify and eliminate these false detections, thereby improving the accuracy of subsequent processing steps.
The DMCL module, on the other hand, is designed to establish reliable connections between text segments. It allows text instances of various shapes to stretch their morphology along their most significant orientation, ensuring that their connections are accurately derived. This is a key aspect of text detection, as it enables the detection of text in non-linear and curved shapes.
Evaluating Performance on Benchmark Datasets
To evaluate the performance of MorphText, the authors conducted extensive experiments on four challenging benchmark datasets: CTW1500, Total-Text, MSRA-TD500, and ICDAR2017. These datasets cover a wide variety of text scenarios, including texts of different shapes, sizes, orientations, and background clutter.
The results of the experiments demonstrate the effectiveness of MorphText in outperforming both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches. This highlights the potential of deep morphology in improving the accuracy and robustness of text detection algorithms.
Relation to Multimedia Information Systems and Virtual Realities
The concepts presented in this article have strong interdisciplinary connections to the fields of multimedia information systems and virtual realities. Text detection is a fundamental component of multimedia information systems, where the accurate extraction and understanding of text from images and videos are essential for effective content retrieval and indexing.
Furthermore, the ability to detect text in arbitrary shapes is particularly important in the context of virtual realities. In virtual reality environments, text may appear on curved surfaces, irregular objects, or within complex scenes. By incorporating deep morphology, as demonstrated in MorphText, virtual reality applications can improve the synthesis of text elements onto these diverse surfaces, enhancing the overall immersion and user experience.
Conclusion
The novel approach presented in this article, MorphText, showcases the potential of deep morphology in addressing the challenges of arbitrary-shape text detection. By leveraging the Deep Morphological Opening and Closing modules, MorphText successfully tackles the accumulation of false text segment detections and the establishment of reliable connections between text segments.
The promising results obtained from benchmark dataset evaluations reinforce the importance of this research in advancing the field of text detection. Furthermore, the interdisciplinary nature of these concepts highlights their relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.