Abstract
Arbitrary shape scene text detection is of great importance in scene understanding tasks. Due to the complexity and diversity of text in natural scenes, existing scene text algorithms have limited accuracy for detecting arbitrary shape text. In this paper, we propose a novel arbitrary shape scene text detector through boundary points dynamic optimization(BPDO). The proposed model is designed with a text aware module (TAM) and a boundary point dynamic optimization module (DOM). Specifically, the model designs a text aware module based on segmentation to obtain boundary points describing the central region of the text by extracting a priori information about the text region. Then, based on the idea of deformable attention, it proposes a dynamic optimization model for boundary points, which gradually optimizes the exact position of the boundary points based on the information of the adjacent region of each boundary point. Experiments on CTW-1500, Total-Text, and MSRA-TD500 datasets show that the model proposed in this paper achieves a performance that is better than or comparable to the state-of-the-art algorithm, proving the effectiveness of the model.
Abstract (translated)
任意形状场景文本检测在场景理解任务中具有重要的意义。由于自然场景中文本的复杂性和多样性，现有的场景文本算法对检测任意形状文本的准确性有限。在本文中，我们通过边界点动态优化（BPDO）提出了一种新颖的任意形状场景文本检测器。与文本感知模块（TAM）和边界点动态优化模块（DOM）相结合，该模型设计了一个基于分段的文本感知模块，以提取文本区域的中间信息。然后，根据变形注意力的思想，它提出了一个动态优化模型来优化边界点的精确位置，该模型根据相邻区域的信息逐步优化边界点的位置。在CTW-1500、Total-Text和MSRA-TD500等数据集上的实验表明，本文提出的模型在实现最佳性能或与最先进的算法相当方面具有优势，证明了该模型的有效性。
URL
https://arxiv.org/abs/2401.09997
