# MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition arXiv: https://arxiv.org/abs/2305.14758 ยท HTML: https://mlsanity.com/paper/2305.14758/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 42 of its 57 references that are in the map. - First posted on arXiv 24 May 2023. ## Builds on (most cited first, 30 of 42) - [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016) - [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016) - [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016) - [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018) - [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017) - [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018) - [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) - [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016) - [BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning](https://mlsanity.com/paper/2002.06715/) (2020) - [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021) - [DER: Dynamically Expandable Representation for Class Incremental Learning](https://mlsanity.com/paper/2103.16788/) (2021) - [DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion](https://mlsanity.com/paper/2111.11326/) (2021) - [Maintaining Discrimination and Fairness in Class Incremental Learning](https://mlsanity.com/paper/1911.07053/) (2019) - [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018) - [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) - [PLOP: Learning without Forgetting for Continual Semantic Segmentation](https://mlsanity.com/paper/2011.11390/) (2020) - [ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019](https://mlsanity.com/paper/1907.00945/) (2019) - [CycleMLP: A MLP-like Architecture for Dense Prediction](https://mlsanity.com/paper/2107.10224/) (2021) - [Distilling Causal Effect of Data in Class-Incremental Learning](https://mlsanity.com/paper/2103.01737/) (2021) - [Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition](https://mlsanity.com/paper/2106.12368/) (2021) - [Continual Learning via Neural Pruning](https://mlsanity.com/paper/1903.04476/) (2019) - [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) - [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://mlsanity.com/paper/1806.00926/) (2018) - [Representation Compensation Networks for Continual Semantic Segmentation](https://mlsanity.com/paper/2203.05402/) (2022) - [Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation](https://mlsanity.com/paper/2204.02136/) (2022) - [GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition](https://mlsanity.com/paper/2002.01276/) (2020) - [Character Region Attention For Text Spotting](https://mlsanity.com/paper/2007.09629/) (2020) - [ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting](https://mlsanity.com/paper/2211.10578/) (2022) ## Built on by (most cited first, 7 of 7) - [Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition](https://mlsanity.com/paper/2405.05841/) (2024) - [Cross-Lingual Learning in Multilingual Scene Text Recognition](https://mlsanity.com/paper/2312.10806/) (2023) - [Decoder Pre-Training with only Text for Scene Text Recognition](https://mlsanity.com/paper/2408.05706/) (2024) - [Relaxed syntax modeling in Transformers for future-proof license plate recognition](https://mlsanity.com/paper/2506.17051/) (2025) - [All-in-One Multilingual Scene Text Recognition with Script-aware Mixture-of-Experts](https://mlsanity.com/paper/2609.24058/) (2026) - [N-gram Injection into Transformers for Dynamic Language Model Adaptation in Handwritten Text Recognition](https://mlsanity.com/paper/2603.03930/) (2026) - [UniLipi: A Unified Multi-Script OCR for Historical Indic Manuscripts](https://mlsanity.com/paper/2608.28195/) (2026) ## Abstract (arXiv) Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages, which makes it difficult to handle newly added languages or adapt to ever-changing data distribution. In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches. IMLTR is particularly challenging due to rehearsal-imbalance, which refers to the uneven distribution of sample characters in the rehearsal set, used to retain a small amount of old data as past memories. To address this issue, we propose a Multiplexed Routing Network (MRN). MRN trains a recognizer for each language that is currently seen. Subsequently, a language domain predictor is learned based on the rehearsal set to weigh the recognizers. Since the recognizers are derived from the original data, MRN effectively reduces the reliance on older data and better fights against catastrophic forgetting, the core issue in IL. We extensively evaluate MRN on MLT17 and MLT19 datasets. It outperforms existing general-purpose IL methods by large margins, with average accuracy improvements ranging from 10.3% to 35.8% under different settings. Code is available at https://github.com/simplify23/MRN.