2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-7.7
Paper Title SEMANTIC-COMPENSATED AND ATTENTION-GUIDED NETWORK FOR SCENE TEXT DETECTION
Authors Yizhan Zhao, Sumei Li, Yueyang Li, Tianjin University, China
SessionMLR-APPL-IP-7: Machine learning for image processing 7
LocationArea E
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, scene text detection based on deep learning has witnessed rapid development. However, most previous methods suffer from (i) semantic information diluted, (ii) false positives in their detections. To alleviate the above dilemmas, we propose an end-to-end trainable text detector named Semantic-compensated and Attention-guided Network (SANet). It contains a Semantic Compensation Module (SCM) and a Text Attention Module (TAM). Specifically, SCM aims to alleviate the dilution of semantic information via compensating the semantic information to the features at each level in a top-down pathway. Furthermore, TAM is employed to encode the strong supervised information into convolutional features, where the text-related features are significantly enhanced by increasing the response gap between text and background. The experimental results on three benchmark datasets, ICDAR 2015, MSRA-TD500, and ICDAR 2017- MLT prove the effectiveness of our method.