# MENTOR: Multilingual tExt detectioN TOward leaRning by analogy

arXiv: https://arxiv.org/abs/2403.07286 · HTML: https://mlsanity.com/paper/2403.07286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 12 Mar 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network](https://mlsanity.com/paper/2002.10200/) (2020)
- [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019)
- [Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting](https://mlsanity.com/paper/2007.09482/) (2020)
- [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018)
- [Learning Open-World Object Proposals without Learning to Classify](https://mlsanity.com/paper/2108.06753/) (2021)
- [Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes](https://mlsanity.com/paper/1904.06535/) (2019)
- [Scene Text Detection with Supervised Pyramid Context Network](https://mlsanity.com/paper/1811.08605/) (2018)
- [MOST: A Multi-Oriented Scene Text Detector with Localization Refinement](https://mlsanity.com/paper/2104.01070/) (2021)
- [E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text](https://mlsanity.com/paper/1801.09919/) (2018)
- [A Multiplexed Network for End-to-End, Multilingual OCR](https://mlsanity.com/paper/2103.15992/) (2021)
- [Benchmarking Scene Text Recognition in Devanagari, Telugu and Malayalam](https://mlsanity.com/paper/2104.04437/) (2021)
- [Geometry Normalization Networks for Accurate Scene Text Detection](https://mlsanity.com/paper/1909.00794/) (2019)
- [An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches](https://mlsanity.com/paper/1804.09003/) (2018)
- [Mask R-CNN with Pyramid Attention Network for Scene Text Detection](https://mlsanity.com/paper/1811.09058/) (2018)
- [On Exploring and Improving Robustness of Scene Text Detection Models](https://mlsanity.com/paper/2110.05700/) (2021)

## Abstract (arXiv)

Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task. For instance, delivery robots in multilingual cities need to be capable of doing multilingual text detection so that the robots can read traffic signs and road markings. Moreover, the target languages change from region to region, implying the need of efficiently re-training the models to recognize the novel/new languages. However, collecting and labeling training data for novel languages are cumbersome, and the efforts to re-train an existing/trained text detector are considerable. Even worse, such a routine would repeat whenever a novel language appears. This motivates us to propose a new problem setting for tackling the aforementioned challenges in a more efficient way: "We ask for a generalizable multilingual text detection framework to detect and identify both seen and unseen language regions inside scene images without the requirement of collecting supervised training data for unseen languages as well as model re-training". To this end, we propose "MENTOR", the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.