Abstract
Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detector, LLE is primarily designed for human vision instead of machine and can accumulate errors. In this work, we propose an efficient and effective single-stage approach for localizing text in dark that circumvents the need for LLE. We introduce a constrained learning module as an auxiliary mechanism during the training stage of the text detector. This module is designed to guide the text detector in preserving textual spatial features amidst feature map resizing, thus minimizing the loss of spatial information in texts under low-light visual degradations. Specifically, we incorporate spatial reconstruction and spatial semantic constraints within this module to ensure the text detector acquires essential positional and contextual range knowledge. Our approach enhances the original text detector's ability to identify text's local topological features using a dynamic snake feature pyramid network and adopts a bottom-up contour shaping strategy with a novel rectangular accumulation technique for accurate delineation of streamlined text features. In addition, we present a comprehensive low-light dataset for arbitrary-shaped text, encompassing diverse scenes and languages. Notably, our method achieves state-of-the-art results on this low-light dataset and exhibits comparable performance on standard normal light datasets. The code and dataset will be released.
Abstract (translated)
在低光环境中定位文本具有挑战性，因为会出现视觉退化。尽管简单的解决方案涉及两个步骤：首先进行低光图像增强（LLE），然后是检测器，但LLE主要针对人类视觉而不是机器，并可能累积错误。在这项工作中，我们提出了一个高效且有效的单阶段方法来在黑暗中定位文本，绕过了需要LLE的步骤。我们在文本检测器的训练阶段引入了一个约束学习模块作为附加机制。这个模块的设计旨在指导文本检测器在特征图缩放过程中保留文本空间特征，从而在低光视觉退化下最小化文本中的空间信息损失。具体来说，我们在这个模块中引入了空间重构和空间语义约束，以确保文本检测器获得了关键的位置和上下文范围知识。我们的方法通过动态蛇特征金字塔网络增强了原始文本检测器的能力，并采用了一种新颖的矩形累积技术，实现了对平滑文本特征的准确边界描绘。此外，我们还提出了一个涵盖任意形状文本的全面低光数据集，包括各种场景和语言。值得注意的是，我们的方法在低光数据集上取得了最先进的成果，同时在标准正常光线数据集上的表现与标准 normal 光线数据集相当。代码和数据集将公开发布。
URL
https://arxiv.org/abs/2404.08965
