# Automatic Script Identification in the Wild

Source: [Automatic Script Identification in the Wild](<https://www.sota2.com/research/paper/68fa61e41e209131ce9f67d1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:08:28.563Z

Authors: Baoguang Shi, Cong Yao, Chengquan Zhang, Xiaowei Guo, Feiyue Huang, Xiang Bai

Published: 2015-05-12T12:38:30.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1505.02982>)

[PDF](<https://arxiv.org/pdf/1505.02982.pdf>)