SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition

Authors

  • Chengwei Zhang Shanghai Jiaotong University
  • Yunlu Xu Hikvision Research Institute
  • Zhanzhan Cheng Zhejiang University Hikvision Research Institute
  • Shiliang Pu Hikvision Research Institute
  • Yi Niu Hikvision Research Institute
  • Fei Wu Zhejiang University
  • Futai Zou Shanghai Jiaotong University

Keywords:

Applications

Abstract

Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Rectification (i.e., spatial transformers) as the preprocessing stage is one popular approach and extensively studied. However, chromatic difficulties in complex scenes have not been paid much attention on. In this work, we introduce a new learnable geometric-unrelated rectification, Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network. This differentiable module can be inserted before any recognition architecture to ease the downstream tasks, giving neural networks the ability to actively transform input intensity rather than only the spatial rectification. It can also serve as a complementary module to known spatial transformations and work in both independent and collaborative ways with them. Extensive experiments show the proposed transformation outperforms existing rectification networks and has comparable performance among the state-of-the-arts.

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Published

2021-05-18

How to Cite

Zhang, C., Xu, Y., Cheng, Z., Pu, S., Niu, Y., Wu, F., & Zou, F. (2021). SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 3305-3314. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16442

Issue

Section

AAAI Technical Track on Computer Vision III