# Automatic Script Identification in the Wild

arXiv: https://arxiv.org/abs/1505.02982 · HTML: https://mlsanity.com/paper/1505.02982/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2015.

## Built on by (most cited first, 5 of 5)

- [A Multiplexed Network for End-to-End, Multilingual OCR](https://mlsanity.com/paper/2103.15992/) (2021)
- [MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition](https://mlsanity.com/paper/2305.14758/) (2023)
- [TeLCoS: OnDevice Text Localization with Clustering of Script](https://mlsanity.com/paper/2104.08045/) (2021)
- [Separate Scene Text Detector for Unseen Scripts is Not All You Need](https://mlsanity.com/paper/2307.15991/) (2023)
- [MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification](https://mlsanity.com/paper/2405.18924/) (2024)

## Abstract (arXiv)

With the rapid increase of transnational communication and cooperation, people frequently encounter multilingual scenarios in various situations. In this paper, we are concerned with a relatively new problem: script identification at word or line levels in natural scenes. A large-scale dataset with a great quantity of natural images and 10 types of widely used languages is constructed and released. In allusion to the challenges in script identification in real-world scenarios, a deep learning based algorithm is proposed. The experiments on the proposed dataset demonstrate that our algorithm achieves superior performance, compared with conventional image classification methods, such as the original CNN architecture and LLC.