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.

在低光环境下本文提出了一种用于定位文本的高效有效的单阶段方法，该方法通过在训练阶段引入约束学习模块，提供文本在低光视觉退化情况下的空间特征保留和定位能力，以获得准确的文本特征描述并在低光数据集上取得了最先进的成果。

黑暗中的文字识别：算法与基准评估

Seeing Text in the Dark: Algorithm and Benchmark

Optical Character Recognition (OCR) systems have been widely used in various
of application scenarios. Designing an OCR system is still a challenging task.
In previous work, we proposed a practical ultra lightweight OCR system (PP-OCR)
to balance the accuracy against the efficiency. In order to improve the
accuracy of PP-OCR and keep high efficiency, in this paper, we propose a more
robust OCR system, i.e. PP-OCRv2. We introduce bag of tricks to train a better
text detector and a better text recognizer, which include Collaborative Mutual
Learning (CML), CopyPaste, Lightweight CPUNetwork (LCNet), Unified-Deep Mutual
Learning (U-DML) and Enhanced CTCLoss. Experiments on real data show that the
precision of PP-OCRv2 is 7% higher than PP-OCR under the same inference cost.
It is also comparable to the server models of the PP-OCR which uses ResNet
series as backbones. All of the above mentioned models are open-sourced and the
code is available in the GitHub repository PaddleOCR which is powered by
PaddlePaddle.

本研究提出了一种更强健的 OCR 系统 PP-OCRv2，采用协作式互相学习、拷贝粘贴、深度神经网络、CTCLoss 等技巧来训练更好的文本探测器和识别器。实验表明，在相同推断成本下，PP-OCRv2 的精度比 PP-OCR 高 7%。

PP-OCRv2: 超轻量 OCR 系统的绝招

PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System

We present a novel single-shot text detector that directly outputs word-level
bounding boxes in a natural image. We propose an attention mechanism which
roughly identifies text regions via an automatically learned attentional map.
This substantially suppresses background interference in the convolutional
features, which is the key to producing accurate inference of words,
particularly at extremely small sizes. This results in a single model that
essentially works in a coarse-to-fine manner. It departs from recent FCN- based
text detectors which cascade multiple FCN models to achieve an accurate
prediction. Furthermore, we develop a hierarchical inception module which
efficiently aggregates multi-scale inception features. This enhances local
details, and also encodes strong context information, allow- ing the detector
to work reliably on multi-scale and multi- orientation text with single-scale
images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench-
mark, advancing the state-of-the-art results in [18, 28]. Demo is available at:
this http URL

本研究提出了一种新颖的单步文字检测器，该检测器直接在自然图像中输出单词级别的边界框。通过自动学习的注意力映射，我们提出了一种注意机制，粗略地识别文本区域。此外，我们还开发了分层 Inception 模块，可以有效地聚合多尺度 Inception 特征，从而使该检测器能够可靠地在单尺度图像上处理多尺度和多方向文本。我们的文本检测器在 ICDAR 2015 基准测试中取得了 77％的 F-measure，超越了先前研究的最新成果。