# ICDAR 2015 Text Reading in the Wild Competition

Source: [ICDAR 2015 Text Reading in the Wild Competition](<https://www.sota2.com/research/paper/68fa61e61e209131ce9f68c8>)

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

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

Authors: Xinyu Zhou, Shuchang Zhou, Cong Yao, Zhimin Cao, Qi Yin

Published: 2015-06-10T06:46:55.000Z

## Abstract

Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community. However, majority of the existing ideas, algorithms and systems are specifically designed for English. This technical report presents the final results of the ICDAR 2015 Text Reading in the Wild (TRW 2015) competition, which aims at establishing a benchmark for assessing detection and recognition algorithms devised for both Chinese and English scripts and providing a playground for researchers from the community. In this article, we describe in detail the dataset, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods. Moreover, promising directions for future research are discussed.

## 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.

- [Scene Text Recognition on TRW-T (test)](<https://www.sota2.com/research/sota/scene-text-recognition-on-trw-t-test.md>): Scene Text Recognition; TRW-T (test); Accuracy: 72.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1506.03184>)

[PDF](<https://arxiv.org/pdf/1506.03184.pdf>)