# A Holistic Representation Guided Attention Network for Scene Text Recognition

arXiv: https://arxiv.org/abs/1904.01375 · HTML: https://mlsanity.com/paper/1904.01375/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [Vision Transformer with Progressive Sampling](https://mlsanity.com/paper/2108.01684/) (2021)
- [ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting](https://mlsanity.com/paper/2211.10578/) (2022)
- [CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition](https://mlsanity.com/paper/2111.11011/) (2021)
- [Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering](https://mlsanity.com/paper/2209.10326/) (2022)
- [Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages](https://mlsanity.com/paper/2111.12276/) (2021)
- [Character decomposition to resolve class imbalance problem in Hangul OCR](https://mlsanity.com/paper/2208.06079/) (2022)
- [SAFL: A Self-Attention Scene Text Recognizer with Focal Loss](https://mlsanity.com/paper/2201.00132/) (2022)
- [Scene Text Recognition with Image-Text Matching-guided Dictionary](https://mlsanity.com/paper/2305.04524/) (2023)

## Abstract (arXiv)

Reading irregular scene text of arbitrary shape in natural images is still a challenging problem, despite the progress made recently. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural networks for sequence modeling. In this work, we propose a simple yet strong approach for scene text recognition. With no need to convert input images to sequence representations, we directly connect two-dimensional CNN features to an attention-based sequence decoder which guided by holistic representation. The holistic representation can guide the attention-based decoder focus on more accurate area. As no recurrent module is adopted, our model can be trained in parallel. It achieves 1.5x to 9.4x acceleration to backward pass and 1.3x to 7.9x acceleration to forward pass, compared with the RNN counterparts. The proposed model is trained with only word-level annotations. With this simple design, our method achieves state-of-the-art or competitive recognition performance on the evaluated regular and irregular scene text benchmark datasets.