# Primitive Representation Learning for Scene Text Recognition

arXiv: https://arxiv.org/abs/2105.04286 · HTML: https://mlsanity.com/paper/2105.04286/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 62 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [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)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [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)
- [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016)
- [Graph-Based Global Reasoning Networks](https://mlsanity.com/paper/1811.12814/) (2018)
- [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://mlsanity.com/paper/2005.10977/) (2020)
- [Focusing Attention: Towards Accurate Text Recognition in Natural Images](https://mlsanity.com/paper/1709.02054/) (2017)
- [ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)](https://mlsanity.com/paper/1708.09585/) (2017)
- [Decoupled Attention Network for Text Recognition](https://mlsanity.com/paper/1912.10205/) (2019)
- [RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition](https://mlsanity.com/paper/2007.07542/) (2020)
- [ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification](https://mlsanity.com/paper/1812.05824/) (2018)
- [Recursive Recurrent Nets with Attention Modeling for OCR in the Wild](https://mlsanity.com/paper/1603.03101/) (2016)
- [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://mlsanity.com/paper/1806.00926/) (2018)
- [SCATTER: Selective Context Attentional Scene Text Recognizer](https://mlsanity.com/paper/2003.11288/) (2020)
- [Scene Text Recognition from Two-Dimensional Perspective](https://mlsanity.com/paper/1809.06508/) (2018)

## Built on by (most cited first, 28 of 28)

- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://mlsanity.com/paper/2207.06966/) (2022)
- [SVTR: Scene Text Recognition with a Single Visual Model](https://mlsanity.com/paper/2205.00159/) (2022)
- [Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition](https://mlsanity.com/paper/2207.00193/) (2022)
- [DTrOCR: Decoder-only Transformer for Optical Character Recognition](https://mlsanity.com/paper/2308.15996/) (2023)
- [CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition](https://mlsanity.com/paper/2111.11011/) (2021)
- [Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features](https://mlsanity.com/paper/2111.15263/) (2021)
- [Self-supervised Character-to-Character Distillation for Text Recognition](https://mlsanity.com/paper/2211.00288/) (2022)
- [Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition](https://mlsanity.com/paper/2112.12916/) (2021)
- [Self-supervised Implicit Glyph Attention for Text Recognition](https://mlsanity.com/paper/2203.03382/) (2022)
- [SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition](https://mlsanity.com/paper/2207.10256/) (2022)
- [Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition](https://mlsanity.com/paper/2402.13643/) (2024)
- [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)
- [Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach](https://mlsanity.com/paper/2308.08806/) (2023)
- [On Manipulating Scene Text in the Wild with Diffusion Models](https://mlsanity.com/paper/2311.00734/) (2023)
- [I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition](https://mlsanity.com/paper/2105.08383/) (2021)
- [Visual-Semantic Transformer for Scene Text Recognition](https://mlsanity.com/paper/2112.00948/) (2021)
- [Searching a High-Performance Feature Extractor for Text Recognition Network](https://mlsanity.com/paper/2209.13139/) (2022)
- [Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition](https://mlsanity.com/paper/2402.15806/) (2024)
- [IterVM: Iterative Vision Modeling Module for Scene Text Recognition](https://mlsanity.com/paper/2204.02630/) (2022)
- [Masked Vision-Language Transformers for Scene Text Recognition](https://mlsanity.com/paper/2211.04785/) (2022)
- [The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing](https://mlsanity.com/paper/2506.06761/) (2025)
- [Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning](https://mlsanity.com/paper/2302.06341/) (2023)
- [Collaborative Chinese Text Recognition with Personalized Federated Learning](https://mlsanity.com/paper/2305.05602/) (2023)
- [Open-Vocabulary Scene Text Recognition via Pseudo-Image Labeling and Margin Loss](https://mlsanity.com/paper/2403.07518/) (2024)
- [Disentanglement and Compositionality of Letter Identity and Letter Position in Variational Auto-Encoder Vision Models](https://mlsanity.com/paper/2412.10446/) (2024)
- [DOTA: Deformable Optimized Transformer Architecture for End-to-End Text Recognition with Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.04175/) (2025)

## Abstract (arXiv)

Scene text recognition is a challenging task due to diverse variations of text instances in natural scene images. Conventional methods based on CNN-RNN-CTC or encoder-decoder with attention mechanism may not fully investigate stable and efficient feature representations for multi-oriented scene texts. In this paper, we propose a primitive representation learning method that aims to exploit intrinsic representations of scene text images. We model elements in feature maps as the nodes of an undirected graph. A pooling aggregator and a weighted aggregator are proposed to learn primitive representations, which are transformed into high-level visual text representations by graph convolutional networks. A Primitive REpresentation learning Network (PREN) is constructed to use the visual text representations for parallel decoding. Furthermore, by integrating visual text representations into an encoder-decoder model with the 2D attention mechanism, we propose a framework called PREN2D to alleviate the misalignment problem in attention-based methods. Experimental results on both English and Chinese scene text recognition tasks demonstrate that PREN keeps a balance between accuracy and efficiency, while PREN2D achieves state-of-the-art performance.