  This paper aims to re-assess scene text recognition (STR) from a
data-oriented perspective. We begin by revisiting the six commonly used
benchmarks in STR and observe a trend of performance saturation, whereby only
2.91% of the benchmark images cannot be accurately recognized by an ensemble of
13 representative models. While these results are impressive and suggest that
STR could be considered solved, however, we argue that this is primarily due to
the less challenging nature of the common benchmarks, thus concealing the
underlying issues that STR faces. To this end, we consolidate a large-scale
real STR dataset, namely Union14M, which comprises 4 million labeled images and
10 million unlabeled images, to assess the performance of STR models in more
complex real-world scenarios. Our experiments demonstrate that the 13 models
can only achieve an average accuracy of 66.53% on the 4 million labeled images,
indicating that STR still faces numerous challenges in the real world. By
analyzing the error patterns of the 13 models, we identify seven open
challenges in STR and develop a challenge-driven benchmark consisting of eight
distinct subsets to facilitate further progress in the field. Our exploration
demonstrates that STR is far from being solved and leveraging data may be a
promising solution. In this regard, we find that utilizing the 10 million
unlabeled images through self-supervised pre-training can significantly improve
the robustness of STR model in real-world scenarios and leads to
state-of-the-art performance.
