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arXiv · 2208.09878

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

Jingyu Lin·Jie Jiang·Yan Yan·Chunchao Guo·Hongfa Wang·Wei Liu·Hanzi Wang
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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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Jingyu Lin, Jie Jiang, Yan Yan, Chunchao Guo, Hongfa Wang, Wei Liu, Hanzi Wang. 2022-08-21. DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection. https://arxiv.org/abs/2208.09878

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