Real-time Detection of Arbitrary Shape Scene Text Based on Segmentation

doi:10.3969/j.issn.1006-2475.2023.11.015

Abstract

Abstract: Abstract：The current challenges of scene text detection technology are mainly reflected in two aspects: the trade-off between model real-time performance and accuracy， and the detection of arbitrary shape text. They determine whether scene text detection is feasible in real scenes. Aiming at the above two problems， this paper proposes a lightweight backbone network with strong feature extraction ability based on segmentation method， which can accurately detect natural scene text of arbitrary shape in real time. Specifically， a simple dual-resolution residual backbone network and a deep aggregate pyramid pooling module with low computational cost are used， and the features extracted from them are fused and segmented using a differentiable binarization module. Through the comparative experiment on the standard English dataset ICDAR2015， the result show that the improved method proposed in this paper is effective， and achieves comparable results in real-time performance and accuracy.

Key words: Key words： real-time text detection, dual resolution backbone, semantic segmentation, deep aggregation pyramid pooling module

CLC Number:

TP391.1

XU Hong-kui, LI Zhen-ye, GUO Wen-tao, ZHAO Jing-zheng, GUO Xu-bin. Real-time Detection of Arbitrary Shape Scene Text Based on Segmentation[J]. Computer and Modernization, 2023, 0(11): 95-100.

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