mlsanityData to 14 Oct 2026

ICDAR 2015 Text Reading in the Wild Competition

arXiv 1506.03184 ↗First posted 10 Jun 2015cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jun 2015.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 2.

8 papers.