scholarly journals An Efficient Text Detection Model for Street Signs

2021 ◽  
Vol 11 (13) ◽  
pp. 5962
Author(s):  
Manhuai Lu ◽  
Yuanxiang Mou ◽  
Chin-Ling Chen ◽  
Qiting Tang
Keyword(s):  
Receptive Field ◽  
Text Detection ◽  
Learning Ability ◽  
Natural Scenes ◽  
Fast Detection ◽  
Detection Model ◽  
Street Sign ◽  
Scene Text ◽  
A Current ◽  
Detector Model

Text detection in natural scenes is a current research hotspot. The Efficient and Accurate Scene Text (EAST) detector model has fast detection speed and good performance but is ineffective in detecting long text regions owing to its small receptive field. In this study, we built upon the EAST model by improving the bounding box’s shrinking algorithm to make the model more accurate in predicting short edges of text regions; altering the loss function from balanced cross-entropy to Focal loss; improving the model’s learning ability on hard, positive examples; and adding a feature enhancement module (FEM) to increase the receptive field of the EAST model and enhance its detection ability for long text regions. The improved EAST model achieved better detection results on both the ICDAR2015 dataset and the Street Sign Text Detection (SSTD) dataset proposed in this paper. The precision and F1 scores of the model also demonstrated advantages over other models on the ICDAR2015 dataset. A comparison of the text detection effects between the improved EAST model and the EAST model showed that the proposed FEM was more effective in increasing the EAST detector’s receptive field, which indicates that it can improve the detection of long text regions.

scholarly journals Scene Text Detection with Supervised Pyramid Context Network

2019 ◽  
Vol 33 ◽  
pp. 9038-9045 ◽  
Author(s):  
Enze Xie ◽  
Yuhang Zang ◽  
Shuai Shao ◽  
Gang Yu ◽  
Cong Yao ◽  
...  
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Text Detection ◽  
Detection Methods ◽  
Natural Scenes ◽  
The Past ◽  
Scene Text Detection ◽  
Scene Text ◽  
Previous State ◽  
Feature Pyramid

Scene text detection methods based on deep learning have achieved remarkable results over the past years. However, due to the high diversity and complexity of natural scenes, previous state-of-the-art text detection methods may still produce a considerable amount of false positives, when applied to images captured in real-world environments. To tackle this issue, mainly inspired by Mask R-CNN, we propose in this paper an effective model for scene text detection, which is based on Feature Pyramid Network (FPN) and instance segmentation. We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.Benefited from the guidance of semantic information and sharing FPN, SPCNET obtains significantly enhanced performance while introducing marginal extra computation. Experiments on standard datasets demonstrate that our SPCNET clearly outperforms start-of-the-art methods. Specifically, it achieves an F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and 82.9% on

scholarly journals SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2657
Author(s):  
Shuangshuang Li ◽  
Wenming Cao
Keyword(s):  
Feature Extraction ◽  
Early Stage ◽  
Text Detection ◽  
Detection Methods ◽  
Good Effect ◽  
Natural Scenes ◽  
Training Time ◽  
Scene Text Detection ◽  
Scene Text ◽  
Curved Text

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.

scholarly journals An Efficient Technique for Scene Text Extraction from Videos

2019 ◽  
Vol 9 (1) ◽  
pp. 6510-6514
Keyword(s):  
Text Detection ◽  
Natural Scenes ◽  
Natural Scene ◽  
Font Size ◽  
Text Extraction ◽  
Scene Text ◽  
Text Information ◽  
Edge Based ◽  
Different Content ◽  
Caption Text

Text Detection from natural scene images and videos is imperative for applications in real world domain analysis. However the text detection process isperplexingbecause of exigent scenarios that the text exhibit. The information present in the video is either perceptual or it is either in semantic form. Amongst the different content that exists in the video, the text information is a major important content that describes more about the nature of the video. The text present in the video can be categorized into Caption text and Scene text. The caption text is the artificial text that is easy to detect while scene text are natural text which is difficult to identify. In this paper text extraction in natural images by edge based method is implemented. The algorithms are estimated with a set of images of natural scenes that differ alongside the scope of font size, illumination, scale and text direction. Precision, accuracy and recall rates are determined to evaluate the performance. The proposed system worked for all difficult scenarios of varied text and gave better results than the existing methods.

scholarly journals TextFuseNet: Scene Text Detection with Richer Fused Features

2020 ◽  
Author(s):  
Jian Ye ◽  
Zhe Chen ◽  
Juhua Liu ◽  
Bo Du
Keyword(s):  
Text Detection ◽  
Feature Representation ◽  
Natural Scenes ◽  
Weak Supervision ◽  
Feature Representations ◽  
General Semantics ◽  
Scene Text Detection ◽  
Scene Text ◽  
Multi Level ◽  
Different Levels

Arbitrary shape text detection in natural scenes is an extremely challenging task. Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection. More specifically, we propose to perceive texts from three levels of feature representations, i.e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection. The multi-level feature representation can adequately describe texts by dissecting them into individual characters while still maintaining their general semantics. TextFuseNet then collects and merges the texts’ features from different levels using a multi-path fusion architecture which can effectively align and fuse different representations. In practice, our proposed TextFuseNet can learn a more adequate description of arbitrary shapes texts, suppressing false positives and producing more accurate detection results. Our proposed framework can also be trained with weak supervision for those datasets that lack character-level annotations. Experiments on several datasets show that the proposed TextFuseNet achieves state-of-the-art performance. Specifically, we achieve an F-measure of 94.3% on ICDAR2013, 92.1% on ICDAR2015, 87.1% on Total-Text and 86.6% on CTW-1500, respectively.

scholarly journals Multi-Directional Scene Text Detection Based on Improved YOLOv3

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4870
Author(s):  
Liyun Xiao ◽  
Peng Zhou ◽  
Ke Xu ◽  
Xiaofang Zhao
Keyword(s):  
Recall Rate ◽  
Detection Algorithm ◽  
Text Detection ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
Practical Application ◽  
Data Set ◽  
Scene Text Detection ◽  
Scene Text ◽  
Detection Effect

To address the problem of low detection rate caused by the close alignment and multi-directional position of text words in practical application and the need to improve the detection speed of the algorithm, this paper proposes a multi-directional text detection algorithm based on improved YOLOv3, and applies it to natural text detection. To detect text in multiple directions, this paper introduces a method of box definition based on sliding vertices. Then, a new rotating box loss function MD-Closs based on CIOU is proposed to improve the detection accuracy. In addition, a step-by-step NMS method is used to further reduce the amount of calculation. Experimental results show that on the ICDAR 2015 data set, the accuracy rate is 86.2%, the recall rate is 81.9%, and the timeliness is 21.3 fps, which shows that the proposed algorithm has a good detection effect on text detection in natural scenes.

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