# Parallel Scale-wise Attention Network for Effective Scene Text Recognition

arXiv: https://arxiv.org/abs/2104.12076 · HTML: https://mlsanity.com/paper/2104.12076/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 48 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020)
- [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016)
- [Focusing Attention: Towards Accurate Text Recognition in Natural Images](https://mlsanity.com/paper/1709.02054/) (2017)
- [Decoupled Attention Network for Text Recognition](https://mlsanity.com/paper/1912.10205/) (2019)
- [ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification](https://mlsanity.com/paper/1812.05824/) (2018)
- [Scene Text Recognition from Two-Dimensional Perspective](https://mlsanity.com/paper/1809.06508/) (2018)
- [Symmetry-constrained Rectification Network for Scene Text Recognition](https://mlsanity.com/paper/1908.01957/) (2019)
- [Reading Scene Text in Deep Convolutional Sequences](https://mlsanity.com/paper/1506.04395/) (2015)
- [Aggregation Cross-Entropy for Sequence Recognition](https://mlsanity.com/paper/1904.08364/) (2019)
- [Edit Probability for Scene Text Recognition](https://mlsanity.com/paper/1805.03384/) (2018)
- [2D Attentional Irregular Scene Text Recognizer](https://mlsanity.com/paper/1906.05708/) (2019)
- [ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images](https://mlsanity.com/paper/2002.12256/) (2020)
- [Deep Structured Output Learning for Unconstrained Text Recognition](https://mlsanity.com/paper/1412.5903/) (2014)
- [Plug-and-Play Rescaling Based Crowd Counting in Static Images](https://mlsanity.com/paper/2001.01786/) (2020)
- [Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting](https://mlsanity.com/paper/2010.01664/) (2020)
- [Stereo Frustums: A Siamese Pipeline for 3D Object Detection](https://mlsanity.com/paper/2010.14599/) (2020)

## Built on by (most cited first, 4 of 4)

- [Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence](https://mlsanity.com/paper/2112.13310/) (2021)
- [A Transformer-based Approach for Arabic Offline Handwritten Text Recognition](https://mlsanity.com/paper/2307.15045/) (2023)
- [A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild](https://mlsanity.com/paper/2105.07625/) (2021)
- [A Discriminative Channel Diversification Network for Image Classification](https://mlsanity.com/paper/2112.05861/) (2021)

## Abstract (arXiv)

The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases.