A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning

Abstract

Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which employs a context attended multi-task learning framework based on a Fully Convolutional Network (FCN) to learn various geometric properties for the reconstruction of polygonal representation of text regions. Taking sequential characteristics of text into consideration, a Context Attention Block is introduced to capture long-range dependencies of pixel information to obtain a more reliable segmentation. In post-processing, a Point-to-Quad assignment method is proposed to cluster pixels into text instances by integrating both high-level object knowledge and low-level pixel information in a single shot. Moreover, the polygonal representation of arbitrarily-shaped text can be extracted with the proposed geometric properties much more effectively. Experiments on several benchmarks, including ICDAR2015, ICDAR2017-MLT, SCUT-CTW1500, and Total-Text, demonstrate that SAST achieves better or comparable performance in terms of accuracy. Furthermore, the proposed algorithm runs at 27.63 FPS on SCUT-CTW1500 with a Hmean of 81.0% on a single NVIDIA Titan Xp graphics card, surpassing most of the existing segmentation-based methods.

arXiv comment9 pages, 6 figures, 7 tables, To appear in ACM Multimedia 2019

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Cited by in corpus (7)

• MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
• StrucTexT: Structured Text Understanding with Multi-Modal Transformers
• Arbitrarily Shaped Scene Text Detection: A Decade of Advances and Systematic Analysis
• TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection
• RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection
• PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer
• Ensemble Learning for Vietnamese Scene Text Spotting in Urban Environments