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

TL;DR

This paper introduces MT, a fast and accurate scene text detection network that effectively detects arbitrary-shaped texts using a lightweight framework, multi-perspective features, and a novel IoU minimization loss.

Contribution

The paper presents a novel multi-perspective feature learning network with a lightweight design and a new loss function for improved scene text detection.

Findings

Outperforms state-of-the-art methods on four datasets

Achieves high detection accuracy with fast inference

Effectively detects arbitrary-shaped texts

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.