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

arXiv: https://arxiv.org/abs/2005.13117 · HTML: https://mlsanity.com/paper/2005.13117/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 13 of 13)

- [Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition](https://mlsanity.com/paper/2207.00193/) (2022)
- [Revisiting Scene Text Recognition: A Data Perspective](https://mlsanity.com/paper/2307.08723/) (2023)
- [PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition](https://mlsanity.com/paper/2109.04145/) (2021)
- [Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features](https://mlsanity.com/paper/2111.15263/) (2021)
- [Self-supervised Implicit Glyph Attention for Text Recognition](https://mlsanity.com/paper/2203.03382/) (2022)
- [Pure Transformer with Integrated Experts for Scene Text Recognition](https://mlsanity.com/paper/2211.04963/) (2022)
- [TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition](https://mlsanity.com/paper/2305.05322/) (2023)
- [Pushing the Performance Limit of Scene Text Recognizer without Human Annotation](https://mlsanity.com/paper/2204.07714/) (2022)
- [TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance](https://mlsanity.com/paper/2111.08314/) (2021)
- [IterVM: Iterative Vision Modeling Module for Scene Text Recognition](https://mlsanity.com/paper/2204.02630/) (2022)
- [DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding](https://mlsanity.com/paper/2207.06695/) (2022)
- [Geometric Perception based Efficient Text Recognition](https://mlsanity.com/paper/2302.03873/) (2023)
- [Ensemble Learning for Vietnamese Scene Text Spotting in Urban Environments](https://mlsanity.com/paper/2404.00852/) (2024)

## Abstract (arXiv)

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. Therefore, methods based on spatial transformers are 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 module, the 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 the existing 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 that the use of SPIN results in a significant improvement on multiple text recognition benchmarks compared to the state-of-the-arts.