# Improving Scene Text Recognition for Character-Level Long-Tailed Distribution arXiv: https://arxiv.org/abs/2304.08592 ยท HTML: https://mlsanity.com/paper/2304.08592/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 48 references that are in the map. - First posted on arXiv 31 Mar 2023. ## Builds on (most cited first, 30 of 32) - [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) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015) - [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019) - [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019) - [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019) - [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017) - [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020) - [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019) - [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020) - [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016) - [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020) - [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) - [Disentangling Label Distribution for Long-tailed Visual Recognition](https://mlsanity.com/paper/2012.00321/) (2020) - [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020) - [ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT](https://mlsanity.com/paper/1909.07741/) (2019) - [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) - [MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2103.12579/) (2021) - [RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition](https://mlsanity.com/paper/2007.07542/) (2020) - [Vision Transformer for Fast and Efficient Scene Text Recognition](https://mlsanity.com/paper/2105.08582/) (2021) - [What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels](https://mlsanity.com/paper/2103.04400/) (2021) - [UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World](https://mlsanity.com/paper/2003.10608/) (2020) - [On Vocabulary Reliance in Scene Text Recognition](https://mlsanity.com/paper/2005.03959/) (2020) - [E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text](https://mlsanity.com/paper/1801.09919/) (2018) - [Revisiting the Importance of Amplifying Bias for Debiasing](https://mlsanity.com/paper/2205.14594/) (2022) - [A Multiplexed Network for End-to-End, Multilingual OCR](https://mlsanity.com/paper/2103.15992/) (2021) ## Built on by (most cited first, 3 of 3) - [Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism](https://mlsanity.com/paper/2403.01832/) (2024) - [Relaxed syntax modeling in Transformers for future-proof license plate recognition](https://mlsanity.com/paper/2506.17051/) (2025) - [N-gram Injection into Transformers for Dynamic Language Model Adaptation in Handwritten Text Recognition](https://mlsanity.com/paper/2603.03930/) (2026) ## Abstract (arXiv) Despite the recent remarkable improvements in scene text recognition (STR), the majority of the studies focused mainly on the English language, which only includes few number of characters. However, STR models show a large performance degradation on languages with a numerous number of characters (e.g., Chinese and Korean), especially on characters that rarely appear due to the long-tailed distribution of characters in such languages. To address such an issue, we conducted an empirical analysis using synthetic datasets with different character-level distributions (e.g., balanced and long-tailed distributions). While increasing a substantial number of tail classes without considering the context helps the model to correctly recognize characters individually, training with such a synthetic dataset interferes the model with learning the contextual information (i.e., relation among characters), which is also important for predicting the whole word. Based on this motivation, we propose a novel Context-Aware and Free Experts Network (CAFE-Net) using two experts: 1) context-aware expert learns the contextual representation trained with a long-tailed dataset composed of common words used in everyday life and 2) context-free expert focuses on correctly predicting individual characters by utilizing a dataset with a balanced number of characters. By training two experts to focus on learning contextual and visual representations, respectively, we propose a novel confidence ensemble method to compensate the limitation of each expert. Through the experiments, we demonstrate that CAFE-Net improves the STR performance on languages containing numerous number of characters. Moreover, we show that CAFE-Net is easily applicable to various STR models.