MT: Multi-Perspective Feature Learning Network for Scene Text Detection

• URL: http://arxiv.org/abs/2105.05455v1
• Date: Wed, 12 May 2021 06:41:34 GMT
• Title: MT: Multi-Perspective Feature Learning Network for Scene Text Detection
• Authors: Chuang Yang, Mulin Chen, Yuan Yuan (Senior Member, IEEE), and Qi Wang (Senior Member, IEEE)
• Abstract summary: A light-weight detection framework is designed to speed up the inference process while keeping high detection accuracy. A multi-perspective feature module is proposed to learn more discriminative representations to segment the mask accurately. The effectiveness of MT is evaluated on four real-world scene text datasets.
• Score: 9.282254601960613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors with different advantages: detection-based models enjoy fast detection speed, and segmentation-based algorithms are not limited by text shapes. However, for most intelligent systems, the detector needs to detect arbitrary-shaped texts with high speed and accuracy simultaneously. Thus, in this study, we design an efficient pipeline named as MT, which can detect adhesive arbitrary-shaped texts with only a single binary mask in the inference stage. This paper presents the contributions on three aspects: (1) a light-weight detection framework is designed to speed up the inference process while keeping high detection accuracy; (2) a multi-perspective feature module is proposed to learn more discriminative representations to segment the mask accurately; (3) a multi-factor constraints IoU minimization loss is introduced for training the proposed model. The effectiveness of MT is evaluated on four real-world scene text datasets, and it surpasses all the state-of-the-art competitors to a large extent.

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