# Separate Scene Text Detector for Unseen Scripts is Not All You Need

arXiv: https://arxiv.org/abs/2307.15991 · HTML: https://mlsanity.com/paper/2307.15991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 29 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Zero-Shot Object Detection](https://mlsanity.com/paper/1804.04340/) (2018)
- [ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019](https://mlsanity.com/paper/1907.00945/) (2019)
- [Zero-Shot Learning by Convex Combination of Semantic Embeddings](https://mlsanity.com/paper/1312.5650/) (2013)
- [Dont Even Look Once: Synthesizing Features for Zero-Shot Detection](https://mlsanity.com/paper/1911.07933/) (2019)
- [Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts](https://mlsanity.com/paper/1803.06049/) (2018)
- [Zero-Shot Object Detection by Hybrid Region Embedding](https://mlsanity.com/paper/1805.06157/) (2018)
- [GTNet: Generative Transfer Network for Zero-Shot Object Detection](https://mlsanity.com/paper/2001.06812/) (2020)
- [TextMountain: Accurate Scene Text Detection via Instance Segmentation](https://mlsanity.com/paper/1811.12786/) (2018)
- [Polarity Loss for Zero-shot Object Detection](https://mlsanity.com/paper/1811.08982/) (2018)
- [Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network](https://mlsanity.com/paper/1801.00470/) (2018)
- [Improving patch-based scene text script identification with ensembles of conjoined networks](https://mlsanity.com/paper/1602.07480/) (2016)
- [Automatic Script Identification in the Wild](https://mlsanity.com/paper/1505.02982/) (2015)
- [A fine-grained approach to scene text script identification](https://mlsanity.com/paper/1602.07475/) (2016)

## Abstract (arXiv)

Text detection in the wild is a well-known problem that becomes more challenging while handling multiple scripts. In the last decade, some scripts have gained the attention of the research community and achieved good detection performance. However, many scripts are low-resourced for training deep learning-based scene text detectors. It raises a critical question: Is there a need for separate training for new scripts? It is an unexplored query in the field of scene text detection. This paper acknowledges this problem and proposes a solution to detect scripts not present during training. In this work, the analysis has been performed to understand cross-script text detection, i.e., trained on one and tested on another. We found that the identical nature of text annotation (word-level/line-level) is crucial for better cross-script text detection. The different nature of text annotation between scripts degrades cross-script text detection performance. Additionally, for unseen script detection, the proposed solution utilizes vector embedding to map the stroke information of text corresponding to the script category. The proposed method is validated with a well-known multi-lingual scene text dataset under a zero-shot setting. The results show the potential of the proposed method for unseen script detection in natural images.