[1]

[cor1]Corresponding author at: College Of Computer Science And Engineering,
Chongqing University of Technology, 400054, China

# Artistic-style text detector and a new Movie-Poster dataset

Aoxiang Ning College Of Computer Science And Engineering, Chongqing University
of Technology, 400054, China<EMAIL_ADDRESS>Yiting Wei
<EMAIL_ADDRESS>College of Agronomy and Biotechnology,
Southwest University, 400715, China  Minglong Xue<EMAIL_ADDRESS>Senming
Zhong<EMAIL_ADDRESS>Industrial Training Center, Guangdong Polytechnic
Normal University, Guangzhou, 510665, China

###### Abstract

Although current text detection algorithms demonstrate effectiveness in
general scenarios, their performance declines when confronted with artistic-
style text featuring complex structures. This paper proposes a method that
utilizes Criss-Cross Attention and residual dense block to address the
incomplete and misdiagnosis of artistic-style text detection by current
algorithms. Specifically, our method mainly consists of a feature extraction
backbone, a feature enhancement network, a multi-scale feature fusion module,
and a boundary discrimination module. The feature enhancement network
significantly enhances the model’s perceptual capabilities in complex
environments by fusing horizontal and vertical contextual information,
allowing it to capture detailed features overlooked in artistic-style text. We
incorporate residual dense block into the Feature Pyramid Network to suppress
the effect of background noise during feature fusion. Aiming to omit the
complex post-processing, we explore a boundary discrimination module that
guides the correct generation of boundary proposals. Furthermore, given that
movie poster titles often use stylized art fonts, we collected a Movie-Poster
dataset to address the scarcity of artistic-style text data. Extensive
experiments demonstrate that our proposed method performs superiorly on the
Movie-Poster dataset and produces excellent results on multiple benchmark
datasets. The code and the Movie-Poster dataset will be available at:
https://github.com/biedaxiaohua/Artistic-style-text-detection

###### keywords:

Text detection Feature enhancement Boundary proposalArtistic-style text

Figure 1: Some samples from the Movie-Poster dataset. Text in (a), (c), and
(d) are highly artistic-style, text in (b) has widely varying aspect ratios,
and in (e), the text area is mixed with background pixels.

##  1 Introduction

Text detection, the process of locating and extracting textual information
from various surfaces such as images, videos, or 3D environments, is a
fundamental component in computer vision and artificial intelligence Long et
al. (2021). In the digital age, where multimedia content is abundant, accurate
text detection is crucial to enhance search capabilities, automate data entry,
and enable intelligent image understanding. Following the groundbreaking
success of convolutional networks exemplified by AlexNet Krizhevsky et al.
(2017), the field of text detection has experienced swift advancement. Several
regression-based methods (Tian et al. (2016); Liao et al. (2017); Zhou et al.
(2017); Wang et al. (2020b)) inspired by general object detection algorithms
(Girshick (2015); Fan et al. (2018, 2019)), employ rectangles or
quadrilaterals for text instance localization. These methods have difficulties
in detecting arbitrary shape text. To represent text instances flexibly,
segmentation-based methods (Deng et al. (2018); Wang et al. (2019c, b); Liao
et al. (2020); Zhu et al. (2021)) treat text detection as a semantic
segmentation task. These methods effectively facilitate text detection in
general scenes. However, specific text environments pose unique challenges
that require further research. As shown in Fig. 1, titles in movie posters are
often artistically rendered to have a variety of shapes. As shown in Fig. 2,
current algorithms have difficulty accurately recognizing and segmenting text
regions, hindering effective feature learning. On the other hand, there is
very little artistic-style text data on the market now. Therefore, detecting
artistic-style text and supplementing artistic-style data is challenging and
meaningful.

In existing datasets such as Total-Text Ch’ng & Chan (2017) and CTW1500
Yuliang et al. (2017), the majority of text instances have a more standardized
shape, with only a few artistic-style text included. Therefore, we collected
the Movie-Poster dataset to enrich the currently scarce artistic-style text
data. Specifically, this dataset contains 1500 movie posters, of which 1100
are used for training and 400 for testing. In our dataset, poster titles are
often treated artistically. As shown in Fig. 1(a), (c), and (d), these titles
are highly personalized and stylized; they can appear at any angle, and there
may be connections and overlaps between characters. As shown in Fig. 1(b) and
(e), some instances have significant differences in aspect ratios, and image
background elements may be mixed with the text, making the detection of text
regions more difficult.

This paper introduces a novel feature enhancement network, primarily composed
of the Recycle Criss-Cross Attention module (RCCA). This module allows for
effective information transfer and interaction between feature maps of
different channels, capturing semantic information about different objects,
textures, and structures in an image to enhance the model’s perceptual
capabilities. It effectively addresses the issue of incomplete detection of
shape-changing artistic-style text, as shown in Fig. 2(a) and (c). TextPMS
Zhang et al. (2022) and TextBPN++ Zhang et al. (2023) fuse the feature maps
output by backbone through the Feature Pyramid Network (FPN), which
effectively improves the representation of features. However, it still suffers
from background noise interference for artistic-style text with complex
backgrounds. Based on this, we design a new multi-scale feature fusion
approach Residual Feature Pyramid Network (R-FPN), combining residual dense
structure and FPN to suppress the effect of redundant information effectively.
As shown in Fig. 2(b), we have effectively solved the problem of incorrectly
recognizing non-text pixels as text pixels. As a mixture of strokes and
background pixels, text regions are highly homogeneous textures that do not
have natural and well-defined boundaries. TextBPN++ Zhang et al. (2023)
systematically presents a unified coarse-to-fine framework via boundary
learning for arbitrary shape text detection. However, this method can lead to
boundary modelling distortions for artistic-style text areas with extreme
aspect ratios. To this end, we explore a Boundary Discriminant Module (BDM)
that guides the generation of boundary proposals by combining the priori
information with the feature maps output from the feature enhancement network.
Subsequently, the boundary proposals are fed into the boundary transformer
module for refinement, thus omitting a series of complex post-processing
procedures.

In summary, our main contributions are four-fold:

  * •

We propose a new feature enhancement network and feature fusion method that
greatly enhances the model’s perceptual capabilities, effectively mitigates
the problem of incomplete detection and suppresses the effect of background
noise.

  * •

We further explore a boundary discrimination module that guides the accurate
generation of boundary proposals.

  * •

We propose the Movie-Poster dataset to complement the existing shortage of
artistic-style text data.

  * •

Extensive experiments have demonstrated that our method achieves state-of-the-
art performance on the Movie-Poster dataset and is competitive on the publicly
available datasets.

The rest of the paper is organized as follows: Sect. 2 overviews the related
work. Sect. 3 elaborates on our work. In Sect. 4, we demonstrate some
experimental results and analysis. Sect. 5 further discussed our proposed
Movie-Poster dataset. Finally, we conclude our work in Sect. 6.

Figure 2: Comparison of the effectiveness of our method with TextBPN++ on
predicting mask maps and boundary proposals for the poster dataset. The
visualization results of the TextBPN++ are on top of the image, and our
method’s results are on the bottom. (a) and (c) demonstrate that we have
solved the problem of incomplete detection. (b) demonstrates that we have
solved the problem of non-text pixels being mistaken for text pixels.

##  2 Related work

With the rapid development of deep learning and object detection technology
(Kim et al. (2018); Kessentini et al. (2019); Jazayeri et al. (2019)),
significant progress has been made in the field of text detection Liu et al.
(2019). Text detection can be broadly categorized into regression-based,
segmentation-based, and connected component-based methods.

###  2.1 Regression-based Methods

The regression-based methods (Wu & Natarajan (2017); Zhou et al. (2017); Liao
et al. (2017); Ma et al. (2018); Wang et al. (2018); Xu et al. (2019); Zhu &
Du (2021); Zhong et al. (2022)) rely on a regression-based object detection
framework with word-level and line-level a priori knowledge. The difficulty of
text detection is that text has irregular shapes with various aspect ratios,
unlike standard images. RRPN Ma et al. (2018) and TextBoxes Liao et al. (2017)
effectively address this problem by predicting anchor offsets to localize text
boxes. Other methods (Zhou et al. (2017); He et al. (2021, 2017b)) are anchor-
free methods that directly regress the offset from a boundary or vertex to the
current point. EAST Zhou et al. (2017) directly predicts text or lines of text
in images with arbitrary orientations and rectangular shapes, eliminating
unnecessary intermediate steps. SAST Wang et al. (2019a) draws on the ideas of
TextSnake Long et al. (2018) and EAST Zhou et al. (2017) and joins the
prediction of some geometric features of the text (the text centerline region,
text boundary bias, and text center bias, etc.) while directly regressing on
the bounding box so that it can be applied to irregular text detection. MOST
He et al. (2021) can dynamically adjust the receptive field of the localized
prediction layer and adaptively merge the raw detections according to the
predicted position, resulting in better detection of instances with large
aspect ratios. TPLAANet Zhong et al. (2022) explores the prediction center
mask to address the challenge of detecting text. Although the regression-based
method is effective in quadrilateral text detection, it is not adapted to
arbitrary shape text detection.

###  2.2 Segmentation-based Methods

Inspired by image segmentation methods (He et al. (2017a); Badrinarayanan et
al. (2017); Schu & Scharcanski (2018); Yang et al. (2020); Farshi et al.
(2020)), segmentation-based text detection methods (Wang et al. (2019b); Liao
et al. (2020, 2022); Li et al. (2024a); Yu et al. (2023)) classify pixels at
the pixel level to discriminate whether each pixel point belongs to a text
target and its connectivity with the surrounding pixels. Then, they integrate
the results of neighbouring pixels into a text box. These methods can be
adapted to any shape and angle of the text. For example, PSENet Wang et al.
(2019b) localizes text of any shape by pixel-level segmentation and uses a
progressive scale expansion algorithm to identify neighbouring text instances.
PAN Wang et al. (2019c) improves on PSENet Wang et al. (2019b) by using a
learnable post-processing method, pixel aggregation, to guide text pixels to
correct kernel parameters by predicted similarity vectors and to reconstruct
complete text instances from the predicted kernel to reconstruct complete text
instances. LSAE Tian et al. (2019) proposed pixel embedding, which groups
pixels based on segmentation results to achieve more accurate text
localization. DBNet Liao et al. (2020) embeds the threshold transformation
process into the network for training by learning the threshold mapping and
employing differentiable operations. In this way, the text detection model can
adaptively learn the thresholds to capture the segmentation information of
text more efficiently. DBNet++ Liao et al. (2022) adds the Adaptive Scale
Fusion (ASF) module to DBNet Liao et al. (2020). Features at different scales
are processed through the ASF module to obtain better-fused features. CBNet
Zhao et al. (2024) uses knowledge distillation to detect arbitrary shape text
efficiently. The most important thing for segmentation-based methods is to
ensure the accuracy of segmentation.

Figure 3: Framework of our method. $F_{P}$ denotes the feature maps output by
Seg-Head, $M^{{}^{\prime}}/M^{{}^{\prime\prime}}$ denotes the masks generated
by BDM, $F_{N}$ denotes the node features obtained by sampling on $F_{P}$,
$P_{n}$ are the proposals after iteratively. We use the Artistic-style
Discriminator to determine whether the predicted detection proposals contain
non-artistic-style text. The Prediction results are the final predicted
boundary proposals.

###  2.3 Connected Component-based Methods

Connected Component-based Methods usually link or group the detected
individual text parts or characters into final text instances through a post-
processing process, for example (Yin et al. (2015); Shi et al. (2017); Baek et
al. (2019); Feng et al. (2019); Tang et al. (2019); Zhang et al. (2020)).
CRAFT Baek et al. (2019) innovatively breaks down the text detection task into
two more recognizable components: fragments and links. In this framework, each
text part is treated as separate fragments, while "links" connect two
neighbouring fragments belonging to the same word, thus enabling complete
detection of the entire word. SegLink Shi et al. (2017), inspired by TextSnake
Long et al. (2018), simply groups detected local text regions by their
geometric relationships. DRRG Zhang et al. (2020) uses Graph Convolutional
Neural Networks Kipf & Welling (2016) (GCN) to learn and infer linking
relationships of text components to group text components. Although linked
component-based methods can work well for arbitrary shape text detection, they
usually have complex post-processing procedures.

##  3 Methodology

###  3.1 Overall network architecture

Our proposed model architecture is shown in Fig. 3. It consists of five main
components: feature extraction backbone, feature enhancement network, feature
fusion module, boundary discrimination module, and boundary transformer.
Specifically, we adopt ResNet-50 He et al. (2016) as the backbone to extract
features. We input the feature maps output from the backbone to the feature
enhancement module, RCCA, which can accurately capture the semantic
information in complex images more acutely. Further, we propose a novel
feature fusion strategy, R-FPN, to integrate the multi-scale feature maps
processed by feature enhancement. After that, the coarse boundary proposals
generated by BDM are utilized to sample the feature maps and extract key node
features. Finally, these features are fed into the boundary transformer for
refinement to achieve more accurate text boundary localization.

###  3.2 Recycle Criss-Cross Attention Module

Current text detection algorithms often encounter issues with incomplete
detection when dealing with artistic-style text. This is because they cannot
effectively capture the complex structural information of artistic-style text.
Therefore, we propose an RCCA module to enhance the model’s perception of
complex structures.

The structure is shown in Fig. 4(a). First, we pass the feature map output
from the backbone through a convolutional layer with 3×3 convolutional kernels
for feature dimensionality reduction, which can effectively reduce redundant
features and model computation. Next, we feed the feature maps into the Criss-
Cross Attention Module Huang et al. (2019), which aggregates contextual
information in the horizontal and vertical directions for each pixel,
enhancing the model’s perceptual capabilities. Then, through a loop operation,
each pixel can eventually capture the global dependencies of all pixels.
Specifically, after a layer of 3×3 convolution, our feature maps are
dimensionalized down to 1/4 of the original ones, and then these feature maps
are fed into the Criss-Cross Attention Module. The structure of the Criss-
Cross Attention Module is shown at the top of Fig. 4(b). In the Criss-Cross
Attention Module, the feature map $I\in{}R^{C\times W\times H}$ is firstly
convolved by two 1×1 convolutions to generate $Q$ and $K$, where ${Q,K}\in
R^{C^{{}^{\prime}}\times W\times H}$, $C^{{}^{\prime}}$ is the number of
channel. After obtaining feature maps $Q$ and $K$, we further generate
attention maps $A\in R^{(H+W-1)\times W\times H}$ via Affinity operation. The
Affinity operation is defined as follows:

$\displaystyle d_{i,u}=Q_{u}\Upsilon_{i,u}$ (1)

where $d_{i,u}$ is the degree of correlation between feature $Q_{u}$ and
$\Upsilon{i,u}$. $Q_{u}\in R^{C^{{}^{\prime}}}$ is the vector of $Q$ at $u$,
$\Upsilon{u}$ are the feature vectors of $K$ which are in the same row or
column with position $u$, $\Upsilon{i,u}$ is the vector of $\Upsilon_{u}$ at
$i$. After getting all the $d_{i,u}$, a softmax operation is performed to the
attention map $A$. Another convolutional layer with 1×1 convolutional kernels
is applied on $I$ to generate $V\in R^{C\times W\times H}$ for feature
adaption. Similarly, at the same position $u$ of $V$, we get the feature
vectors $V_{u}\in R^{C}$, and finally, we get a set $\Psi_{u}\in
R^{(H+W-1)\times C}$, which is the set extracted from the same rows and
columns as $u$ in $V$. Further, we get the semantic information of the
duplicate rows and columns at position u through the aggregation operation,
which is defined as follows:

$\displaystyle
I_{u}^{{}^{\prime}}=\sum_{i\in\left|\Psi_{u}\right|}A_{i,u}\Psi_{i,u}+I_{u}$
(2)

where $I_{u}^{{}^{\prime}}$ is a feature vector in output maps
$I^{{}^{\prime}}\in R^{C\times W\times H}$. $A_{i,u}$ is a scalar value at
channel $i$ and position $u$ in $A$. The contextual information is added to
local feature $I$ to enhance the local features and augment the pixel-wise
representation.

As shown at the bottom of Fig. 4(b), since the remote context information is
only captured horizontally and vertically in the Criss-Cross Attention Module,
and the pixel-to-pixel connections around the pixel are still sparse, we
designed a recycle module where the output feature map $I^{{}^{\prime}}$ after
passing through the first module is fed to the next Criss-Cross Attention
Module so that our feature maps $I^{{}^{\prime\prime}}$ contain global context
information. The operation is defined as follows:

$\displaystyle I^{{}^{\prime\prime}}=Att(I^{{}^{\prime}})$ (3)

where $Att$ denotes the operation of Criss-Cross Attention,
$I^{{}^{\prime\prime}}\in R^{C\times W\times H}$ is the feature map output
after two $Att$ operations.

Then $I^{{}^{\prime\prime}}$ again undergo a convolution operation and concat
with $I$ to get the final output feature maps $D\in R^{C\times W\times H}$.

Figure 4: (a) The overall framework of the feature enhancement network
includes four layers of the RCCA module. $I$, $I^{{}^{\prime}}$,
$I^{{}^{\prime\prime}}$, and $D$ denote the feature maps after various
operations. (b) The Criss-Cross Attention Module framework.

###  3.3 Residual Feature Pyramid Network

Since artistic-style text is often mixed with background pixels, the current
method usually mistakes the background for the text. Therefore, we propose an
FPN-based feature fusion method, R-FPN, to suppress the effect of background
noise. Its structure is shown in Fig. 5(a).

Specifically, we change the input at the third layer of the fusion stage and
feed the feature map $D\in R^{C\times W\times H}$ output from the feature
enhancement network into our proposed Redundant Feature Reduction Module
(RFRM) to obtain a clean feature map $D^{{}^{\prime}}\in R^{C\times W\times
H}$, which is then fed into the feature pyramid network for multiscale fusion.
The structure of the RFRM is shown in Fig. 5(b). In RFRM, we first pass a
feature map of size 512×512 through a convolutional layer with 3×3
convolutional kernels to better capture the global information. Then, we use
the Residual Dense Block (RDB) with a four-layer structure to get the
background noise features $O\in R^{C^{{}^{\prime}}\times W\times H}$. The RDB
consists of residual structure and ReLU operation. It extracts rich local
features through densely connected convolutional layers and introduces local
residual learning to improve the information flow further. The residual
operation is next used to fuse the features obtained from the RDB and ReLU
combination, effectively preventing the long-term dependency problem. After
the second RDB module and the ReLU operation, we perform an element-wise
addition operation on the input raw features and the features from the first
and second RDB block to get the final redundant features $O^{{}^{\prime}}\in
R^{C^{{}^{\prime}}\times W\times H}$. Finally, we subtract the redundant
features obtained by RFRM from the original input features to obtain a clean
feature map $D^{{}^{\prime}}$, and then input $D^{{}^{\prime}}\in R^{C\times
W\times H}$ into FPN for feature fusion, which is defined by RFRM as follows:

$\displaystyle D^{{}^{\prime}}=D-CRC(O+O^{{}^{\prime}})$ (4)

where $D^{{}^{\prime}}\in R^{C\times W\times H}$ is the feature map after
denoising, $C$ denotes a function of Conv, $R$ express a function of ReLU.

###  3.4 Boundary Discrimination Module

Due to irregular shapes and extreme aspect ratios, artistic-style text can
suffer from boundary modelling distortion. In this regard, we propose the BDM
to solve this problem.

Specifically, we map ground truth onto the input image to obtain a mask map
$M\in R^{C\times W\times H}$. Based on $M$, we compute the Euclidean Distance
transform from each pixel point in the image to the nearest nonzero pixel
point to obtain a new mask map $M^{{}^{\prime}}\in R^{C\times W\times H}$.
From the output mask map $M^{{}^{\prime}}$, we can compute the coarse boundary
proposals $P_{0}$. However, the initially generated coarse boundary proposal
may be erroneous due to some images’ highly irregular shapes of artistic-style
text regions. This will introduce noise during training and affect the model’s
performance. Therefore, we design a discriminator to solve this problem. If
the number of nonzero pixels in $M^{{}^{\prime}}$ is much lower or higher than
the number of nonzero pixels in $M$, it is determined that its modelling
fails. If it fails, the Euclidean Distance transform between each pixel point
in the ground truth mask and the nearest nonzero pixel point is computed to
obtain the mask map $M^{{}^{\prime\prime}}$, and then the coarse boundary
proposals are generated based on $M^{{}^{\prime\prime}}$.

Figure 5: (a) The overall structure of R-FPN. The 1/64, 1/256, 1/1024, and
1/2048 indicate the scale ratio compared to the input image. (b) Detailed
architecture of RFRM in R-FPN. Figure 6: Some analytical visualizations of the
Movie-Poster dataset. (a) Complexity of the text region in Movie-Poster. We
judge the complexity of a text region by the number of coordinate points, with
coordinate points between 10 and 15 being Complex, coordinate points between
15 and 30 being Moderately Complex, and coordinate points than 30 being
Extremely Complex. (b) Area distribution of all text regions in Movie-Poster,
where the unit is square pixels.

$P_{0}=\left\\{\begin{array}[]{lr}f(M^{{}^{\prime\prime}}),&\frac{N_{M^{{}^{\prime}}}}{N_{M}}<th_{a}\
or\ \frac{N_{M^{{}^{\prime}}}}{N_{M}}>th_{b}\\\
f(M^{{}^{\prime}}),&th_{a}<=\frac{N_{M^{{}^{\prime}}}}{N_{M}}<=th_{b}\\\
\end{array}\right.$ (5)

where $f$ stands for the function of calculates the coarse boundary proposals
based on the mask map; $N_{M}$, $N_{M^{{}^{\prime}}}$ denotes the number of
nonzero pixels in $M$ and $M^{{}^{\prime}}$; $th_{a}$ and $th_{b}$ is the
threshold that is experimentally proven to perform best when $th_{a}=0.25$ and
$th_{b}=1.75$;

The Seg-Head consists of a multilayer expansion convolution, including two 3 ×
3 convolution layers with different expansion rates and one 1 × 1 convolution
layer. With the Seg-Head, we obtain the feature maps $F_{P}\in R^{C\times
W\times H}$: the classification map, the distance field map, and the direction
field map. We root $P_{0}$ to sample the specified location of the feature
maps $F_{P}$ to obtain the node features $F_{N}$. Then $F_{N}$ are fed into
the boundary transformer for n iterations of refinement to obtain the final
boundary proposals $P_{n}$.

Table 1: Ablation study of R-FPN, RCCA, BDM and Artistic-style Discriminator. ’$\checkmark$’ means use the module, and ‘$\times$’ means don’t use it. Bold indicates the best result, while underline indicates second. Methods | Movie-Poster | Total-Text
---|---|---
R-FPN | RDM | RCCA | Discriminator | Precision | Recall | F-measure | Precision | Recall | F-measure
$\times$ | $\times$ | $\times$ | $\times$ | 71.92 | 86.83 | 78.67 | 91.1 | 83.4 | 87.08
$\checkmark$ | $\times$ | $\times$ | $\times$ | 81.48 | 85.85 | 83.61 | 90.4 | 84.3 | 87.26
$\times$ | $\checkmark$ | $\times$ | $\times$ | 84.67 | 84.87 | 84.77 | 91.12 | 83.4 | 87.09
$\times$ | $\times$ | $\checkmark$ | $\times$ | 86.84 | 83.66 | 85.22 | 90.5 | 84.86 | 87.59
$\checkmark$ | $\times$ | $\checkmark$ | $\times$ | 81.95 | 89.76 | 85.68 | 90.27 | 85.53 | 87.84
$\checkmark$ | $\checkmark$ | $\checkmark$ | $\times$ | 83.76 | 88.05 | 85.85 | 90.17 | 85.67 | 87.86
$\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | 88.89 | 85.85 | 87.34 | - | - | -
Table 2: Ablation study of the number of Criss-Cross Attention Module cycles. ’P’, ’R’, and ’F’ represent Precision, Recall, and F-measure. Bold indicates the best result, while underline indicates second. | Movie-Poster
---|---
Cycles num. | Precision | Recall | F-measure
0 | 81.5 | 85.89 | 83.64
1 | 87.28 | 83.66 | 85.43
2 | 88.89 | 85.85 | 87.43
3 | 88.49 | 84.39 | 86.39

##  4 Experimental results

###  4.1 Datasets

Movie-Poster: It contains 1500 images, of which 1100 are in the training set
and 400 in the testing set. The data consists of movie posters, most of which
are artistic-style text of arbitrary shapes. These text regions can appear at
any angle, and there may be joins and overlaps between characters. Fig. 6
illustrates some of the analysis of the Movie-Poster dataset. This shows that
our dataset contains text boxes of various shapes and sizes, which is highly
challenging. In addition, the Movie-Poster dataset is multilingual, mainly in
English and Chinese.

Total-Text (Ch’ng & Chan (2017)): It is a dataset containing the text of
various shapes, including horizontal, multi-orientational, and curved. The
dataset has a total of 1555 images and 11459 text lines. There are 1255 images
in the training set and 300 images in the testing set.

CTW1500 (Yuliang et al. (2017)): It dataset contains 1,500 images, of which
1,000 are for training and 500 are for testing. Each image has at least one
curved text, containing many horizontal and multi-orientational text. In
addition, the dataset is multilingual, with mainly Chinese and English text.

MSRA-TD500 (Yao et al. (2012)): It contains 500 natural scene images, of which
300 are for training and 200 are for testing. It supports multiple languages,
mainly Chinese and English. It contains a variety of text orientations and
covers diverse scenes, such as billboards, signs, and walls.

ICDAR-Art (Chng et al. (2019)): It contains a total of 10,166 images, 5603
images in the training set and 4563 images in the testing set. It consists of
three parts: Total-Text, SCUT-CTW1500, and Baidu Curved Scene Text
(ICDAR2019-LSVT partially curved data), and contains horizontal, multi-
orientational, and curved text of various shapes.

###  4.2 Implementation Details

The ResNet-50 He et al. (2016) is adopted as our backbone. In our experiments,
we randomly crop the text region and resize it to 640 × 640 for training the
model. To demonstrate the model’s performance, we do not use pre-training
data, and the initial learning rate is set to 0.0001 or 0.001 and decays by
0.9 after every 50 epochs. For all datasets, the training batch size is set to
4, and we use Adam Kingma & Ba (2014) as the optimizer. The data augmentation
includes random rotation with an angle, random cropping, and random flipping.
The other datasets were trained with 600 epochs except for MSRA-TD500, which
was trained with 1200 epochs. In the inference, we keep the aspect ratio of
the test images and then resize and fill them to the same size for the test.
Training and testing are performed on a single GPU (NVIDIA GeForce RTX3090).

Table 3: Detection results on the Movie-Poster dataset. Bold indicates the best result, while underline indicates second. † denotes the result reproduced using the original paper method. Methods | Published | Movie-Poster/IOU 0.5 | Movie-Poster/IOU 0.75
---|---|---|---
|  | Precision | Recall | F-measure | Precision | Recall | F-measure
PSENet†Wang et al. (2019b) | CVPR’19 | 79.54 | 84.39 | 81.89 | 60.68 | 64.39 | 62.48
FAST†Chen et al. (2021) | Arxiv’21. | 74.65 | 78.29 | 76.42 | 46.38 | 48.53 | 47.43
PAN†Wang et al. (2019c) | ICCV’19 | 82.23 | 82.43 | 82.33 | 63.26 | 63.41 | 63.33
DBNet++†Liao et al. (2022) | TPAMI’22 | 80.2 | 77.45 | 78.8 | - | - | -
TextPMS†Zhang et al. (2022) | TPAMI’23 | 75.75 | 86.09 | 80.59 | 62.01 | 70.48 | 65.98
TextBPN++†Zhang et al. (2023) | T-MM’23 | 71.92 | 86.83 | 78.67 | 44.84 | 54.14 | 49.06
CBNet†Zhao et al. (2024) | IJCV’24 | 87.89 | 81.46 | 84.55 | 68.68 | 63.65 | 66.07
Ours | - | 88.89 | 85.85 | 87.34 | 70.7 | 68.29 | 69.47

###  4.3 Ablation study

We perform a series of ablation experiments on the Movie-Poster dataset and
Total-Text to demonstrate the effectiveness of our proposed modules. A
detailed study of the performance of each module is given in Table 1.

Effectiveness of RCCA: Table 1 demonstrates the effectiveness of the RCCA
module. On the Movie-Poster dataset, the use of the RCCA module alone results
in a significant increase in F-measure by about 6.55$\%$. This proves that
RCCA can effectively utilize the global semantic information to better adapt
to complex environments, significantly improving the model’s effectiveness in
detecting artistic-style text. In addition, for the Total-Text dataset, our
F-measure is also increased by 0.51$\%$, proving that the module is useful for
detecting curved text.

Effectiveness of R-FPN: The effectiveness of the R-FPN can be seen from the
results in Table 1. Only using this module on the Movie-Poster and Total-Text
datasets improves the F-measure by 4.94$\%$ and 0.18$\%$, respectively. If
both R-FPN and RCCA are used, the increment of F-measure reaches 0.78$\%$ on
the totaltext and 7.01$\%$ on the Movie-Poster.

Effectiveness of BDM: BDM is also effective on the Artistic Poster dataset, as
shown in Table 1. If only BDM is used, the increment of F-measure reaches
6.1$\%$. This shows that the correctness of boundary modelling is essential
for detecting artistic-style text, and BDM effectively guarantees the
correctness of boundary modelling. Since the text instances in the Total-Text
have a more regular shape, BDM has relatively little impact on this dataset,
with a 0.01$\%$ increase in F-measure. Our method uses R-FPN, RCCA, and BDM,
increasing F-measure by 0.78$\%$ on Total-Text and 7.18$\%$ on Movie-Poster.

Effectiveness of the number of Criss-Cross Attention Module cycles: As shown
in Table 2, the detection effect is optimized when the number of cycles is set
to 2. The analysis yields that the Criss-Cross Attention Module collects
semantic information in each cycle’s horizontal and vertical directions. When
the number of cycles is 1, the connection between pixels is still sparse. Each
pixel incorporates global semantic information by increasing the number of
cycles to 2. However, if the number of cycles is increased to 3 or more, too
much noise may be introduced, which leads to a decrease in the recall.
Therefore, our method sets the number of cycles to 2 to balance the
information’s comprehensiveness with noise control.

When detecting the Movie-Poster dataset, we designed an Artistic-style
Discriminator to determine whether the detected text region is an artistic-
style title, effectively removing those redundant text boxes. The F-measure
increases by 1.49$\%$, reaching 87.34$\%$, and the results are shown in Table
1.

Figure 7: Qualitative comparisons with CBNetZhao et al. (2024), DBNet++Liao et
al. (2022), and PSENetWang et al. (2019b).

###  4.4 Comparisons with previous methods

To comprehensively evaluate the performance of our proposed method, we
conducted comparative experiments on five different datasets. These datasets
include the Movie-Poster dataset and four recognized benchmark datasets:
Total-Text, CTW1500, ICDAR-Art, and MSRA-TD500. For a comprehensive
evaluation, we employed both qualitative and quantitative methods.

Movie-Poster: During training, we configured the input image dimensions to 640
× 640 and employed the Adam Kingma & Ba (2014) optimizer. We commenced the
training process without leveraging any pre-trained data, setting the initial
learning rate to 0.0001 and applying an exponential decay of 0.9 every 50
epochs. Given that the artistic-style text in the Movie-Poster dataset
contains a variety of glyphs of larger sizes, we set intersection and
concurrency (IOU) thresholds of 0.5 and 0.75 respectively when testing. The
quantitative results are shown in Table LABEL:tab3. Our method achieved
promising results in Precision (88.89$\%$), Recall (85.85$\%$) and F-measure
(87.34$\%$). The qualitative results are shown in Fig. 7. Compared with the
state-of-the-art methods (Liao et al. (2022); Zhang et al. (2022, 2023); Zhao
et al. (2024)), we have a clear advantage, and all indicators have achieved
the most advanced results. When evaluating the F-measure, our method improves
the performance by 8.67$\%$ compared to the baseline Zhang et al. (2023) when
the intersection and concurrency ratio (IOU) threshold is set to 0.5 and
improves the performance by 2.79$\%$ compared to the current state-of-the-art
method CBNet Zhao et al. (2024). When the IOU threshold is increased to 0.75,
our method further improves the performance by 20.41$\%$ compared to the
baseline Zhang et al. (2023), and 3.4$\%$ compared to the CBNet Zhao et al.
(2024). This shows the effectiveness of our method for artistic-style text
detection.

Table 4: Detection results on the Total-Text dataset. ’P’, ’R’, and ’F’
represent Precision, Recall, and F-measure. Bold indicates the best result,
while underline indicates second. † denotes the result reproduced using the
original paper method.

Methods | Published | P | R | F
---|---|---|---|---
SPCNetXie et al. (2019) | AAAI’19 | 83 | 82.8 | 82.9
LOMOZhang et al. (2019) | CVPR’19 | 87.6 | 79.3 | 83.3
PSENet†Wang et al. (2019b) | CVPR’19 | 88.55 | 77.81 | 82.83
PAN†Wang et al. (2019c) | ICCV’19 | 83.54 | 77.45 | 80.38
ContourNetWang et al. (2020b) | CVPR’20 | 86.9 | 83.9 | 85.4
DRRGZhang et al. (2020) | CVPR’20 | 86.5 | 84.9 | 85.7
PAN++†Wang et al. (2021) | TPAMI’21 | 76.21 | 69.05 | 72.46
FAST†Chen et al. (2021) | Arxiv’21 | 87.44 | 79.29 | 83.17
DBNet++†Liao et al. (2022) | TPAMI’22 | 87.48 | 78.94 | 82.3
TextPMS†Zhang et al. (2022) | TPAMI’22 | 85.66 | 83.28 | 84.55
EMAZhao et al. (2022) | TIP’22 | 83.3 | 88.9 | 86
TextBPN++†Zhang et al. (2023) | T-MM’23 | 91.1 | 83.4 | 87.08
CBNet†Zhao et al. (2024) | IJCV’24 | 87.53 | 80.21 | 83.71
Ours | - | 90.17 | 85.67 | 87.86

Table 5: Detection results on the CTW1500 dataset. ’P’, ’R’, and ’F’ represent
Precision, Recall, and F-measure. Bold indicates the best result, while
underline indicates second. † denotes the result reproduced using the original
paper method.

Methods | Published | P | R | F
---|---|---|---|---
PSENet†Wang et al. (2019b) | CVPR’19 | 82.06 | 77.97 | 79.96
LOMOZhang et al. (2019) | CVPR’19 | 89.2 | 69.6 | 78.4
PAN†Wang et al. (2019c) | ICCV’19 | 78.18 | 78.85 | 78.51
TextRayWang et al. (2020a) | MM’20 | 77.9 | 83.5 | 80.6
PAN++†Wang et al. (2021) | TPAMI’21 | 74.18 | 74.8 | 74.49
TextPMS†Zhang et al. (2022) | TPAMI’22 | 84.02 | 78.32 | 81.07
FAST†Chen et al. (2021) | Arxiv’21 | 83.51 | 76.27 | 79.73
TextBPN++†Zhang et al. (2023) | T-MM’23 | 87 | 79.63 | 83.15
Ours | - | 81.59 | 83.8 | 83.12

Table 6: Detection results on the MSRA-TD500 dataset. ’P’, ’R’, and ’F’
represent Precision, Recall, and F-measure. Bold indicates the best result,
while underline indicates second. † denotes the result reproduced using the
original paper method.

Methods | Published | P | R | F
---|---|---|---|---
PAN†Wang et al. (2019c) | ICCV’19 | 58.7 | 68.03 | 63.02
TextPMS†Zhang et al. (2022) | TPAMI’22 | 69.79 | 74.23 | 71.94
FAST†Chen et al. (2021) | Chen et al. | 75.42 | 68.59 | 72.04
TextBPN++†Zhang et al. (2023) | T-MM’23 | 72.17 | 74.4 | 73.27
CBNet†Zhao et al. (2024) | IJCV’24 | 75.13 | 76.79 | 75.95
Ours | - | 75.38 | 76 | 75.8

Table 7: Detection results on the ICDAR-Art dataset. ’P’, ’R’, and ’F’
represent Precision, Recall, and F-measure. Bold indicates the best result,
while underline indicates second. † denotes the result reproduced using the
original paper method.

Methods | Published | P | R | F
---|---|---|---|---
PSENet†Wang et al. (2019b) | CVPR’19 | 81.1 | 57.5 | 67.3
ContourNetWang et al. (2020b) | CVPR’20 | 62.1 | 73.2 | 67.2
DBNetLiao et al. (2020) | AAAI’20 | 56 | 69.9 | 62.2
TextRayWang et al. (2020a) | MM’20 | 58.6 | 75.97 | 66.17
PCRDai et al. (2021) | CVPR’21 | 65 | 83.6 | 73.1
EMAZhao et al. (2022) | TIP’22 | 68.7 | 80.8 | 74.3
Wang et al.†Wang et al. (2022) | TIP’23 | 60.6 | 78.35 | 68.4
Ours | - | 70.5 | 80.37 | 75.11

Total-Text: During training, we initialize the learning rate to 0.001, and
other parameters are the same as training the Movie-Poster dataset. The
quantitative results are shown in Table 4. Regarding the F-measure, our
proposed method demonstrates a 0.78$\%$ enhancement in performance over the
baseline Zhang et al. (2023) and a notable 4.15$\%$ improvement when compared
with CBNet Zhao et al. (2024). The experimental outcomes provide compelling
evidence of the efficacy of our methodology in detecting and processing curved
text.

Figure 8: Some visual results of failure cases. In (a) and (b), these text
regions are very large in shape and more similar in color to the background.
In (c), our method does not exclude the redundant text boxes in this poster.
In (d), the detection fails because a character image splits the overall text
region.

CTW1500: Similarly, during training, we configured the input image dimensions
to 640 × 640 and employed the Adam Kingma & Ba (2014) optimizer. We commenced
the training process without leveraging any pre-trained data, setting the
initial learning rate to 0.0001 and applying an exponential decay of 0.9 every
50 epochs. As shown in Table 5, the quantitative analysis results on the
CTW1500 dataset indicate that our proposed method exhibits similar performance
to TextBPN++ Zhang et al. (2023). Further comparing with method TextPMS Zhang
et al. (2022), our F-measure achieves a 2.05$\%$ improvement.

MSRA-TD500: Considering the limited amount of data in the dataset MSRA-TD500,
we performed 1200 epochs during the training process and set the initial
learning rate to 0.0001. In the testing stage, we adjusted the size of the
input image to limit it to 640 × 960 pixels. As illustrated in the Table 6,
our proposed method achieves a 2.6$\%$ improvement in the F-measure compared
to the TextBPN++Zhang et al. (2023). In addition, we are also competitive with
the state-of-the-art method CBNetZhao et al. (2024).

ICDAR-Art: To substantiate the generalizability of our proposed method, we
conducted experiments on the ICDAR-Art dataset, which includes a large number
of real-world instances of curved text. As shown in Table 7, our method
outperforms current state-of-the-art models. Specifically, the F-measure of
our method is 0.81$\%$ higher than EMA Zhao et al. (2022) and 6.71$\%$ higher
than the newer method Wang et al. (2022).

###  4.5 Challenges

While our methodology exhibits robust performance in detecting artistic-style
text, it encounters challenges when dealing with more extreme textual
variations. As shown in Fig. 8(a) and (b), our method still needs to be
improved when dealing with text with extreme shapes and highly blended colors
with the background. As shown in Fig. 8(c), there are few cases of error
detection, categorizing non-artistic-style text errors as artistic style
titles. Meanwhile, in Fig. 8(d), the severe occlusion of the character image
resulted in incomplete text detection. We invite you to come up with
innovative solutions to meet these challenges.

##  5 Discussion

Current text detection methods primarily focus on general scenarios, with few
addressing the detection of artistic-style text. However, the importance of
detecting artistic-style text in our lives has become increasingly apparent.
We collected 1,500 movie posters featuring various artistic-style titles to
address the current market’s lack of artistic-style text data, and we
conducted a comprehensive data analysis of this dataset. We evaluated the
complexity of each text region based on the number of coordinate points. As
shown in Fig. 6(a), the complexity is roughly divided into three categories:
Complex, Moderately Complex, and Extremely Complex. The complexity
distribution in this dataset is relatively balanced. Fig. 6(b) shows the area
distribution of all annotated text boxes, revealing a wide range of text
region sizes in our dataset, including various irregularly shaped artistic-
style texts. This further proves the representativeness of our dataset. While
our method represents a significant step forward in detecting artistic-style
text, Sec. 4.5 discusses areas for improvement. We hope to achieve more
efficient and faster detection of artistic-style text in the future.

##  6 Conclusion

This paper proposes a novel method that effectively enhances the model’s
perceptual capabilities to accurately detect artistic-style text with complex
structures, filling the gap in current text detection algorithms for detecting
artistic-style text. In our method, the RCCA module, which consists of the
Criss-Cross Attention Module, effectively utilizes global contextual
information to enable the model to capture detailed features that are highly
similar to the background. R-FPN based on FPN and residual dense block
suppresses the effect of background noise and avoids treating non-text pixels
as text pixels. BDM ensures the correctness of boundary modelling. We propose
the Movie-Poster dataset to fill the market’s gap in artistic-style text data.
Extensive experiments demonstrate that our proposed method performs superiorly
on the Movie-Poster dataset and produces excellent results on multiple
benchmark datasets.

## Acknowledgements

This work was supported in part by the Chongqing University of Technology
high-quality development Action Plan for of graduate education gzlcx20243151,
the Basic and Applied Basic Research Foundation of Guangdong Province under
Project 2021A1515110298, and in part by the Science and Technology Program of
Nansha under Project 2021ZD003

## References

  * Badrinarayanan et al. (2017) Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017).  Segnet: A deep convolutional encoder-decoder architecture for image segmentation.  IEEE transactions on pattern analysis and machine intelligence, 39, 2481–2495.
  * Baek et al. (2019) Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019).  Character region awareness for text detection.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9365–9374).
  * Chen et al. (2021) Chen, Z., Wang, J., Wang, W., Chen, G., Xie, E., Luo, P., & Lu, T. (2021).  Fast: Faster arbitrarily-shaped text detector with minimalist kernel representation.  arXiv preprint arXiv:2111.02394, .
  * Ch’ng & Chan (2017) Ch’ng, C. K., & Chan, C. S. (2017).  Total-text: A comprehensive dataset for scene text detection and recognition.  In 2017 14th IAPR international conference on document analysis and recognition (ICDAR) (pp. 935–942).  IEEE volume 1.
  * Chng et al. (2019) Chng, C. K., Liu, Y., Sun, Y., Ng, C. C., Luo, C., Ni, Z., Fang, C., Zhang, S., Han, J., Ding, E. et al. (2019).  Icdar2019 robust reading challenge on arbitrary-shaped text-rrc-art.  In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 1571–1576).  IEEE.
  * Dai et al. (2021) Dai, P., Zhang, S., Zhang, H., & Cao, X. (2021).  Progressive contour regression for arbitrary-shape scene text detection.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7393–7402).
  * Deng et al. (2018) Deng, D., Liu, H., Li, X., & Cai, D. (2018).  Pixellink: Detecting scene text via instance segmentation.  In Proceedings of the AAAI conference on artificial intelligence.  volume 32.
  * Dong et al. (2023) Dong, Y., Jiang, Z., Tao, F., & Fu, Z. (2023).  Multiple spatial residual network for object detection.  Complex & Intelligent Systems, 9, 1347–1362.
  * Fan et al. (2018) Fan, D.-P., Cheng, M.-M., Liu, J.-J., Gao, S.-H., Hou, Q., & Borji, A. (2018).  Salient objects in clutter: Bringing salient object detection to the foreground.  In Proceedings of the European conference on computer vision (ECCV) (pp. 186–202).
  * Fan et al. (2019) Fan, D.-P., Wang, W., Cheng, M.-M., & Shen, J. (2019).  Shifting more attention to video salient object detection.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8554–8564).
  * Farshi et al. (2020) Farshi, T. R., Drake, J. H., & Özcan, E. (2020).  A multimodal particle swarm optimization-based approach for image segmentation.  Expert Systems with Applications, 149, 113233.
  * Feng et al. (2019) Feng, W., He, W., Yin, F., Zhang, X.-Y., & Liu, C.-L. (2019).  Textdragon: An end-to-end framework for arbitrary shaped text spotting.  In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9076–9085).
  * Girshick (2015) Girshick, R. (2015).  Fast r-cnn.  In Proceedings of the IEEE international conference on computer vision (pp. 1440–1448).
  * Han et al. (2021) Han, Q., Yin, Q., Zheng, X., & Chen, Z. (2021).  Remote sensing image building detection method based on mask r-cnn.  Complex & Intelligent Systems, (pp. 1–9).
  * He et al. (2017a) He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017a).  Mask r-cnn.  In Proceedings of the IEEE international conference on computer vision (pp. 2961–2969).
  * He et al. (2016) He, K., Zhang, X., Ren, S., & Sun, J. (2016).  Deep residual learning for image recognition.  In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
  * He et al. (2021) He, M., Liao, M., Yang, Z., Zhong, H., Tang, J., Cheng, W., Yao, C., Wang, Y., & Bai, X. (2021).  Most: A multi-oriented scene text detector with localization refinement.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8813–8822).
  * He et al. (2017b) He, W., Zhang, X.-Y., Yin, F., & Liu, C.-L. (2017b).  Deep direct regression for multi-oriented scene text detection.  In Proceedings of the IEEE international conference on computer vision (pp. 745–753).
  * Huang et al. (2019) Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., & Liu, W. (2019).  Ccnet: Criss-cross attention for semantic segmentation.  In Proceedings of the IEEE/CVF international conference on computer vision (pp. 603–612).
  * Jazayeri et al. (2019) Jazayeri, S., Saghafi, A., Esmaeili, S., & Tsokos, C. P. (2019).  Automatic object detection using dynamic time warping on ground penetrating radar signals.  Expert Systems with Applications, 122, 102–107.
  * Kessentini et al. (2019) Kessentini, Y., Besbes, M. D., Ammar, S., & Chabbouh, A. (2019).  A two-stage deep neural network for multi-norm license plate detection and recognition.  Expert systems with applications, 136, 159–170.
  * Kim et al. (2018) Kim, J. H., Batchuluun, G., & Park, K. R. (2018).  Pedestrian detection based on faster r-cnn in nighttime by fusing deep convolutional features of successive images.  Expert Systems with Applications, 114, 15–33.
  * Kingma & Ba (2014) Kingma, D. P., & Ba, J. (2014).  Adam: A method for stochastic optimization.  arXiv preprint arXiv:1412.6980, .
  * Kipf & Welling (2016) Kipf, T. N., & Welling, M. (2016).  Semi-supervised classification with graph convolutional networks.  arXiv preprint arXiv:1609.02907, .
  * Krizhevsky et al. (2017) Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017).  Imagenet classification with deep convolutional neural networks.  Communications of the ACM, 60, 84–90.
  * Li et al. (2024a) Li, T., Bai, J., & Wang, Q. (2024a).  Enhancing medical text detection with vision-language pre-training and efficient segmentation.  Complex & Intelligent Systems, (pp. 1–13).
  * Li et al. (2024b) Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024b).  A deep learning model for steel surface defect detection.  Complex & Intelligent Systems, 10, 885–897.
  * Liao et al. (2017) Liao, M., Shi, B., Bai, X., Wang, X., & Liu, W. (2017).  Textboxes: A fast text detector with a single deep neural network.  In Proceedings of the AAAI conference on artificial intelligence.  volume 31.
  * Liao et al. (2020) Liao, M., Wan, Z., Yao, C., Chen, K., & Bai, X. (2020).  Real-time scene text detection with differentiable binarization.  In Proceedings of the AAAI conference on artificial intelligence (pp. 11474–11481).  volume 34.
  * Liao et al. (2022) Liao, M., Zou, Z., Wan, Z., Yao, C., & Bai, X. (2022).  Real-time scene text detection with differentiable binarization and adaptive scale fusion.  IEEE transactions on pattern analysis and machine intelligence, 45, 919–931.
  * Liu et al. (2019) Liu, X., Meng, G., & Pan, C. (2019).  Scene text detection and recognition with advances in deep learning: a survey.  International Journal on Document Analysis and Recognition (IJDAR), 22, 143–162.
  * Liu & Jin (2017) Liu, Y., & Jin, L. (2017).  Deep matching prior network: Toward tighter multi-oriented text detection.  In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1962–1969).
  * Long et al. (2021) Long, S., He, X., & Yao, C. (2021).  Scene text detection and recognition: The deep learning era.  International Journal of Computer Vision, 129, 161–184.
  * Long et al. (2018) Long, S., Ruan, J., Zhang, W., He, X., Wu, W., & Yao, C. (2018).  Textsnake: A flexible representation for detecting text of arbitrary shapes.  In Proceedings of the European conference on computer vision (ECCV) (pp. 20–36).
  * Ma et al. (2018) Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., & Xue, X. (2018).  Arbitrary-oriented scene text detection via rotation proposals.  IEEE transactions on multimedia, 20, 3111–3122.
  * Schu & Scharcanski (2018) Schu, G., & Scharcanski, J. (2018).  A new method for building adaptive bayesian trees and its application in color image segmentation.  Expert Systems with Applications, 98, 57–71.
  * Shi et al. (2017) Shi, B., Bai, X., & Belongie, S. (2017).  Detecting oriented text in natural images by linking segments.  In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2550–2558).
  * Tang et al. (2019) Tang, J., Yang, Z., Wang, Y., Zheng, Q., Xu, Y., & Bai, X. (2019).  Seglink++: Detecting dense and arbitrary-shaped scene text by instance-aware component grouping.  Pattern recognition, 96, 106954.
  * Tian et al. (2016) Tian, Z., Huang, W., He, T., He, P., & Qiao, Y. (2016).  Detecting text in natural image with connectionist text proposal network.  In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14 (pp. 56–72).  Springer.
  * Tian et al. (2019) Tian, Z., Shu, M., Lyu, P., Li, R., Zhou, C., Shen, X., & Jia, J. (2019).  Learning shape-aware embedding for scene text detection.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4234–4243).
  * Tursun et al. (2020) Tursun, O., Denman, S., Zeng, R., Sivapalan, S., Sridharan, S., & Fookes, C. (2020).  Mtrnet++: One-stage mask-based scene text eraser.  Computer Vision and Image Understanding, 201, 103066.
  * Wang et al. (2020a) Wang, F., Chen, Y., Wu, F., & Li, X. (2020a).  Textray: Contour-based geometric modeling for arbitrary-shaped scene text detection.  In Proceedings of the 28th ACM international conference on multimedia (pp. 111–119).
  * Wang et al. (2022) Wang, F., Xu, X., Chen, Y., & Li, X. (2022).  Fuzzy semantics for arbitrary-shaped scene text detection.  IEEE Transactions on Image Processing, 32, 1–12.
  * Wang et al. (2018) Wang, F., Zhao, L., Li, X., Wang, X., & Tao, D. (2018).  Geometry-aware scene text detection with instance transformation network.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1381–1389).
  * Wang et al. (2019a) Wang, P., Zhang, C., Qi, F., Huang, Z., En, M., Han, J., Liu, J., Ding, E., & Shi, G. (2019a).  A single-shot arbitrarily-shaped text detector based on context attended multi-task learning.  In Proceedings of the 27th ACM international conference on multimedia (pp. 1277–1285).
  * Wang et al. (2019b) Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., & Shao, S. (2019b).  Shape robust text detection with progressive scale expansion network.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9336–9345).
  * Wang et al. (2021) Wang, W., Xie, E., Li, X., Liu, X., Liang, D., Yang, Z., Lu, T., & Shen, C. (2021).  Pan++: Towards efficient and accurate end-to-end spotting of arbitrarily-shaped text.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 5349–5367.
  * Wang et al. (2019c) Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., Yu, G., & Shen, C. (2019c).  Efficient and accurate arbitrary-shaped text detection with pixel aggregation network.  In Proceedings of the IEEE/CVF international conference on computer vision (pp. 8440–8449).
  * Wang et al. (2020b) Wang, Y., Xie, H., Zha, Z.-J., Xing, M., Fu, Z., & Zhang, Y. (2020b).  Contournet: Taking a further step toward accurate arbitrary-shaped scene text detection.  In proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11753–11762).
  * Wu & Natarajan (2017) Wu, Y., & Natarajan, P. (2017).  Self-organized text detection with minimal post-processing via border learning.  In proceedings of the IEEE international conference on computer vision (pp. 5000–5009).
  * Xie et al. (2019) Xie, E., Zang, Y., Shao, S., Yu, G., Yao, C., & Li, G. (2019).  Scene text detection with supervised pyramid context network.  In Proceedings of the AAAI conference on artificial intelligence (pp. 9038–9045).  volume 33.
  * Xu et al. (2019) Xu, Y., Wang, Y., Zhou, W., Wang, Y., Yang, Z., & Bai, X. (2019).  Textfield: Learning a deep direction field for irregular scene text detection.  IEEE Transactions on Image Processing, 28, 5566–5579.
  * Xue et al. (2019) Xue, C., Lu, S., & Zhang, W. (2019).  Msr: multi-scale shape regression for scene text detection.  arXiv preprint arXiv:1901.02596, .
  * Yang et al. (2020) Yang, Y., Feng, C., & Wang, R. (2020).  Automatic segmentation model combining u-net and level set method for medical images.  Expert Systems with Applications, 153, 113419.
  * Yao et al. (2012) Yao, C., Bai, X., Liu, W., Ma, Y., & Tu, Z. (2012).  Detecting texts of arbitrary orientations in natural images.  In 2012 IEEE conference on computer vision and pattern recognition (pp. 1083–1090).  IEEE.
  * Yin et al. (2015) Yin, X.-C., Pei, W.-Y., Zhang, J., & Hao, H.-W. (2015).  Multi-orientation scene text detection with adaptive clustering.  IEEE transactions on pattern analysis and machine intelligence, 37, 1930–1937.
  * Yu et al. (2023) Yu, W., Liu, Y., Hua, W., Jiang, D., Ren, B., & Bai, X. (2023).  Turning a clip model into a scene text detector.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6978–6988).
  * Yuliang et al. (2017) Yuliang, L., Lianwen, J., Shuaitao, Z., & Sheng, Z. (2017).  Detecting curve text in the wild: New dataset and new solution.  arXiv preprint arXiv:1712.02170, .
  * Zhang et al. (2019) Zhang, C., Liang, B., Huang, Z., En, M., Han, J., Ding, E., & Ding, X. (2019).  Look more than once: An accurate detector for text of arbitrary shapes.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10552–10561).
  * Zhang et al. (2023) Zhang, S.-X., Yang, C., Zhu, X., & Yin, X.-C. (2023).  Arbitrary shape text detection via boundary transformer.  IEEE Transactions on Multimedia, .
  * Zhang et al. (2022) Zhang, S.-X., Zhu, X., Chen, L., Hou, J.-B., & Yin, X.-C. (2022).  Arbitrary shape text detection via segmentation with probability maps.  IEEE transactions on pattern analysis and machine intelligence, 45, 2736–2750.
  * Zhang et al. (2020) Zhang, S.-X., Zhu, X., Hou, J.-B., Liu, C., Yang, C., Wang, H., & Yin, X.-C. (2020).  Deep relational reasoning graph network for arbitrary shape text detection.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9699–9708).
  * Zhao et al. (2022) Zhao, M., Feng, W., Yin, F., Zhang, X.-Y., & Liu, C.-L. (2022).  Mixed-supervised scene text detection with expectation-maximization algorithm.  IEEE Transactions on Image Processing, 31, 5513–5528.
  * Zhao et al. (2024) Zhao, X., Feng, W., Zhang, Z., Lv, J., Zhu, X., Lin, Z., Hu, J., & Shao, J. (2024).  Cbnet: A plug-and-play network for segmentation-based scene text detection.  International Journal of Computer Vision, (pp. 1–20).
  * Zhong et al. (2022) Zhong, D., Lyu, S., Shivakumara, P., Pal, U., & Lu, Y. (2022).  Text proposals with location-awareness-attention network for arbitrarily shaped scene text detection and recognition.  Expert Systems with Applications, 205, 117564.
  * Zhou et al. (2017) Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., & Liang, J. (2017).  East: an efficient and accurate scene text detector.  In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 5551–5560).
  * Zhu et al. (2021) Zhu, Y., Chen, J., Liang, L., Kuang, Z., Jin, L., & Zhang, W. (2021).  Fourier contour embedding for arbitrary-shaped text detection.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3123–3131).
  * Zhu & Du (2021) Zhu, Y., & Du, J. (2021).  Textmountain: Accurate scene text detection via instance segmentation.  Pattern Recognition, 110, 107336.