CVSep 16, 2019

ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)

arXiv:1909.07145v1263 citations
Originality Synthesis-oriented
AI Analysis

It provides a benchmark for arbitrary-shaped text reading in computer vision, but is incremental as it builds on existing competition frameworks.

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT), which tackled scene text detection, recognition, and spotting tasks, with top scores ranging from 53.86% to 85.32% across five sub-challenges.

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants methods. The dataset, the evaluation kit as well as the results are publicly available at https://rrc.cvc.uab.es/?ch=14

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Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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