JMNET: Arbitrary-shaped scene text detection using multi-space perception

Zhijian Lin

doi:10.1016/j.neucom.2022.09.095

2022-11-01

PRE

Zhijian Lin

Ying Chen

陈萍萍 (Pingping Chen) *

陈宏辉 (Honghui Chen)

Feng Chen

Nam Ling

DOI：10.1016/j.neucom.2022.09.095

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摘要

En 中文

The arbitrary-shape scene text detection faces enormous challenges in accuracy and speed with increas-ing demand in the industrial sector. Although most studies have introduced segmentation and achieved remarkable performance in text detection tasks, researchers ignored the importance of detection effi-ciency. This paper proposes a new Joint Multi-Space Perception Network (JMNET) for efficient scene text detection to address this issue. Based on a lightweight feature extraction backbone, we put forward two novel modules, i.e., Scale Spatial Perception Module (SSPM) and Attention Spatial Perception Module (ASPM) to enhance the expression ability of text features with low computational complexity. Moreover, we propose an Unsupervised Embedding Spatial Perception Loss function (UnESP Loss) by introducing the Euclidean distance measurement between the embeddings to overcome the ambiguity of text instance boundary, such as a small line spacing. In this way, text embedding learning is not restricted by specific shapes, and detection robustness can be improved. Extensive experiments on four benchmarks, including the ICDAR2015, MSRA-TD500, CTW1500 and Totaltext, show that the proposed JMNET achieves competitive performance in terms of both accuracy and speed over the state-of-the-art methods. Particularly, our method can reach a remarkable F-measure of 85.5% at 52 FPS on Totaltext. Code is available at:https://github.com/sakura-910/JMNET. (c) 2022 Elsevier B.V. All rights reserved.

Keyword:

Deep learning

Scene text detection

Multi -space perception

Joint network