# Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition

arXiv: https://arxiv.org/abs/2305.05140 · HTML: https://mlsanity.com/paper/2305.05140/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [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)
- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://mlsanity.com/paper/2005.10977/) (2020)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Focusing Attention: Towards Accurate Text Recognition in Natural Images](https://mlsanity.com/paper/1709.02054/) (2017)
- [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://mlsanity.com/paper/2108.09661/) (2021)
- [SVTR: Scene Text Recognition with a Single Visual Model](https://mlsanity.com/paper/2205.00159/) (2022)
- [RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition](https://mlsanity.com/paper/2007.07542/) (2020)
- [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://mlsanity.com/paper/1806.00926/) (2018)
- [TextScanner: Reading Characters in Order for Robust Scene Text Recognition](https://mlsanity.com/paper/1912.12422/) (2019)
- [GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition](https://mlsanity.com/paper/2002.01276/) (2020)
- [CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition](https://mlsanity.com/paper/2111.11011/) (2021)
- [Multi-Granularity Prediction for Scene Text Recognition](https://mlsanity.com/paper/2209.03592/) (2022)
- [2D Attentional Irregular Scene Text Recognizer](https://mlsanity.com/paper/1906.05708/) (2019)

## Built on by (most cited first, 18 of 18)

- [SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition](https://mlsanity.com/paper/2411.15858/) (2024)
- [Context Perception Parallel Decoder for Scene Text Recognition](https://mlsanity.com/paper/2307.12270/) (2023)
- [Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing](https://mlsanity.com/paper/2405.04377/) (2024)
- [Instruction-Guided Scene Text Recognition](https://mlsanity.com/paper/2401.17851/) (2024)
- [Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition](https://mlsanity.com/paper/2310.04999/) (2023)
- [How Control Information Influences Multilingual Text Image Generation and Editing?](https://mlsanity.com/paper/2407.11502/) (2024)
- [Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition](https://mlsanity.com/paper/2405.05841/) (2024)
- [Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing](https://mlsanity.com/paper/2411.15585/) (2024)
- [What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution](https://mlsanity.com/paper/2602.06450/) (2026)
- [Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods](https://mlsanity.com/paper/2606.24484/) (2026)
- [Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition](https://mlsanity.com/paper/2407.05562/) (2024)
- [All-in-One Multilingual Scene Text Recognition with Script-aware Mixture-of-Experts](https://mlsanity.com/paper/2609.24058/) (2026)
- [Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition](https://mlsanity.com/paper/2410.09913/) (2024)
- [OTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition](https://mlsanity.com/paper/2511.08133/) (2025)
- [SELECT: Detecting Label Errors in Real-world Scene Text Data](https://mlsanity.com/paper/2512.14050/) (2025)
- [Multi-Modal Character Localization and Extraction for Chinese Text Recognition](https://mlsanity.com/paper/2603.13886/) (2026)
- [DocRevive: A Unified Pipeline for Document Text Restoration](https://mlsanity.com/paper/2604.10077/) (2026)
- [Embedding Rotation Invariance for Provable Multi-Oriented Scene Text Recognition](https://mlsanity.com/paper/2608.10684/) (2026)

## Abstract (arXiv)

Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task. However, due to lacking the perception of linguistic knowledge and information, recent vision models suffer from two problems: (1) the pure vision-based query results in attention drift, which usually causes poor recognition and is summarized as linguistic insensitive drift (LID) problem in this paper. (2) the visual feature is suboptimal for the recognition in some vision-missing cases (e.g. occlusion, etc.). To address these issues, we propose a Linguistic Perception Vision model (LPV), which explores the linguistic capability of vision model for accurate text recognition. To alleviate the LID problem, we introduce a Cascade Position Attention (CPA) mechanism that obtains high-quality and accurate attention maps through step-wise optimization and linguistic information mining. Furthermore, a Global Linguistic Reconstruction Module (GLRM) is proposed to improve the representation of visual features by perceiving the linguistic information in the visual space, which gradually converts visual features into semantically rich ones during the cascade process. Different from previous methods, our method obtains SOTA results while keeping low complexity (92.4% accuracy with only 8.11M parameters). Code is available at https://github.com/CyrilSterling/LPV.