DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection

• URL: http://arxiv.org/abs/2208.09878v1
• Date: Sun, 21 Aug 2022 12:58:45 GMT
• Title: DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection
• Authors: Jingyu Lin, Jie Jiang, Yan Yan, Chunchao Guo, Hongfa Wang, Wei Liu, Hanzi Wang
• Abstract summary: We propose DPTNet, a simple yet effective architecture to model the global and local information for the scene text detection task. We propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path. Our DPTNet achieves state-of-the-art results on the MSRA-TD500 dataset, and provides competitive results on other standard benchmarks in terms of both detection accuracy and speed.
• Score: 34.42038300372715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prosperity of deep learning contributes to the rapid progress in scene text detection. Among all the methods with convolutional networks, segmentation-based ones have drawn extensive attention due to their superiority in detecting text instances of arbitrary shapes and extreme aspect ratios. However, the bottom-up methods are limited to the performance of their segmentation models. In this paper, we propose DPTNet (Dual-Path Transformer Network), a simple yet effective architecture to model the global and local information for the scene text detection task. We further propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path. Moreover, a bi-directional interaction module across the two paths is developed to provide complementary clues in the channel and spatial dimensions. We also upgrade the concentration operation by adding an extra multi-head attention layer to it. Our DPTNet achieves state-of-the-art results on the MSRA-TD500 dataset, and provides competitive results on other standard benchmarks in terms of both detection accuracy and speed.

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