Artistic text recognition is an extremely challenging task with a wide range
of applications. However, current scene text recognition methods mainly focus
on irregular text while have not explored artistic text specifically. The
challenges of artistic text recognition include the various appearance with
special-designed fonts and effects, the complex connections and overlaps
between characters, and the severe interference from background patterns. To
alleviate these problems, we propose to recognize the artistic text at three
levels. Firstly, corner points are applied to guide the extraction of local
features inside characters, considering the robustness of corner structures to
appearance and shape. In this way, the discreteness of the corner points cuts
off the connection between characters, and the sparsity of them improves the
robustness for background interference. Secondly, we design a character
contrastive loss to model the character-level feature, improving the feature
representation for character classification. Thirdly, we utilize Transformer to
learn the global feature on image-level and model the global relationship of
the corner points, with the assistance of a corner-query cross-attention
mechanism. Besides, we provide an artistic text dataset to benchmark the
performance. Experimental results verify the significant superiority of our
proposed method on artistic text recognition and also achieve state-of-the-art
performance on several blurred and perspective datasets.

提出了一种利用角点、字符对比损失、Transformer 和全局特征建模方法的艺术文本识别方法，并建立了一个新的数据集来衡量性能，实验结果显示该方法在艺术文本识别上显著优于现有方法且在几个数据集上取得了最优性能。

理解 WordArt：基于角点引导的 Transformer 用于场景文本识别

Toward Understanding WordArt: Corner-Guided Transformer for Scene Text  Recognition

In this work, we contribute an EM algorithm for estimation of corner points
and linear crossing segments for both marked and unmarked pedestrian crosswalks
using the detections of pedestrians from processed LiDAR point clouds or camera
images. We demonstrate the algorithmic performance by analyzing three
real-world datasets containing multiple periods of data collection for
four-corner and two-corner intersections with marked and unmarked crosswalks.
Additionally, we include a Python video tool to visualize the crossing
parameter estimation, pedestrian trajectories, and phase intervals in our
public source code.

本文提出了一种用于估计标记和未标记的人行横道的拐角点和线性交叉段的 EM 算法，使用处理过的 LiDAR 点云或摄像头图像中的行人检测，并通过分析三个现实世界数据集来展示算法性能，此外还提供了一个 Python 视频工具来可视化交叉参数估计、行人轨迹和相位间隔。

从行人检测到人行横道估计：多样数据集上的 EM 算法和分析

From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets

Previous deep learning based state-of-the-art scene text detection methods
can be roughly classified into two categories. The first category treats scene
text as a type of general objects and follows general object detection paradigm
to localize scene text by regressing the text box locations, but troubled by
the arbitrary-orientation and large aspect ratios of scene text. The second one
segments text regions directly, but mostly needs complex post processing. In
this paper, we present a method that combines the ideas of the two types of
methods while avoiding their shortcomings. We propose to detect scene text by
localizing corner points of text bounding boxes and segmenting text regions in
relative positions. In inference stage, candidate boxes are generated by
sampling and grouping corner points, which are further scored by segmentation
maps and suppressed by NMS. Compared with previous methods, our method can
handle long oriented text naturally and doesn't need complex post processing.
The experiments on ICDAR2013, ICDAR2015, MSRA-TD500, MLT and COCO-Text
demonstrate that the proposed algorithm achieves better or comparable results
in both accuracy and efficiency. Based on VGG16, it achieves an F-measure of
84.3% on ICDAR2015 and 81.5% on MSRA-TD500.

该论文提出了一种利用文本边框的角点定位和定位相对位置的文本区域分割方法，结合了一般性物体检测和文本区域分割的优点，避免了它们的缺点，并在 ICDAR2013，ICDAR2015，MSRA-TD500，MLT 和 COCO-Text 上进行了实验，得出了比以前更好或相当的结果。