Segmentation-based scene text detection algorithms that are accurate to the pixel level can satisfy the detection of arbitrary shape scene text and have received widespread attention. On the one hand, due to the complexity and di-versity of the scene text, the convolution with a fixed kernel size has some limitations in extracting the visual features of the scene text. On the other hand, most of the existing segmentation-based algorithms only segment the center of the text, losing information such as the edges and directions of the text, with limited detection accuracy. There are also some improved algorithms that use iterative cor-rections or introduce other multiple information to improve text detection accuracy but at the expense of efficiency. To address these issues, this paper proposes a simple and effective scene text detection method, the Kernel Adaptive Con-volution, which is designed with a Kernel Adaptive Con-volution Module for scene text detection via predicting the distance map. Specifically, first, we design an extensible kernel adaptive convolution module (KACM) to extract vi-sual features from multiple convolutions with different ker-nel sizes in an adaptive manner. Secondly, our method pre-dicts the text distance map under the supervision of a pri-ori information (including direction map, and foreground segmentation map) and completes the text detection from the predicted distance map. Experiments on four publicly available datasets prove the effectiveness of our algorithm, in which the accuracy and efficiency of both the Total- Text and TD500 outperform the state-of-the-art algorithm. The algorithm efficiency is improved while the accuracy is com-petitive on ArT and CTW1500.

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 MSRATD500 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.

Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention. Existing segmentation-based scene text detection algorithms usually only segment the pixels in the center region of the text, while ignoring other information of the text region, such as edge information, distance information, etc., thus limiting the detection accuracy of the algorithm for scene text. This paper proposes a plug-and-play module called the Region Multiple Information Perception Module (RMIPM) to enhance the detection performance of segmentation-based algorithms. Specifically, we design an improved module that can perceive various types of information about scene text regions, such as text foreground classification maps, distance maps, direction maps, etc. Experiments on MSRA-TD500 and TotalText datasets show that our method achieves comparable performance with current state-of-the-art algorithms.