Abstract
Scene text detection has seen the emergence of high-performing methods that excel on academic benchmarks. However, these detectors often fail to replicate such success in real-world scenarios. We uncover two key factors contributing to this discrepancy through extensive experiments. First, a \textit{Fine-tuning Gap}, where models leverage \textit{Dataset-Specific Optimization} (DSO) paradigm for one domain at the cost of reduced effectiveness in others, leads to inflated performances on academic benchmarks. Second, the suboptimal performance in practical settings is primarily attributed to the long-tailed distribution of texts, where detectors struggle with rare and complex categories as artistic or overlapped text. Given that the DSO paradigm might undermine the generalization ability of models, we advocate for a \textit{Joint-Dataset Learning} (JDL) protocol to alleviate the Fine-tuning Gap. Additionally, an error analysis is conducted to identify three major categories and 13 subcategories of challenges in long-tailed scene text, upon which we propose a Long-Tailed Benchmark (LTB). LTB facilitates a comprehensive evaluation of ability to handle a diverse range of long-tailed challenges. We further introduce MAEDet, a self-supervised learning-based method, as a strong baseline for LTB. The code is available at this https URL.
Abstract (translated)
场景文本检测领域已涌现出一系列在学术基准测试中表现出色的方法，然而这些方法往往难以在实际环境中复制同样的成功。通过广泛的实验研究，我们发现了导致这一差距的两个关键因素。首先，存在一个称为“微调间隙”（Fine-tuning Gap）的现象：模型利用特定数据集优化（Dataset-Specific Optimization, DSO）范式为某一领域量身定制时，会导致在其他领域的有效性降低，并且这种做法会使得模型在学术基准测试中的表现被夸大。其次，在实际应用中性能不佳主要是由于文本的长尾分布导致的，即检测器难以处理罕见和复杂的类别，如艺术性或重叠的文字。 鉴于DSO范式可能削弱模型的泛化能力，我们提倡采用联合数据集学习（Joint-Dataset Learning, JDL）协议来缓解微调间隙问题。此外，我们还进行了一项错误分析，识别出长尾场景文本中的三个主要挑战类别和十三个子类别，并提出一个长尾基准测试（Long-Tailed Benchmark, LTB），以全面评估处理多样化的长尾挑战的能力。 为了进一步支持这一研究，我们引入了MAEDet方法作为LTB的一个强基线，该方法基于自监督学习。相关的代码可以在以下链接中获取：[此URL]（请将实际的GitHub或其他代码托管平台的URL填入括号内）。
URL
https://arxiv.org/abs/2505.15649
