MFECN: Multi-level Feature Enhanced Cumulative Network for Scene Text Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-22
Author(s):  
Zhandong Liu ◽  
Wengang Zhou ◽  
Houqiang Li
Keyword(s):  
Text Detection ◽  
Detection Algorithms ◽  
Features Fusion ◽  
Irregular Shapes ◽  
Scene Text Detection ◽  
Scene Text ◽  
Text Information ◽  
Multi Level ◽  
Public Datasets ◽  
High Level

Recently, many scene text detection algorithms have achieved impressive performance by using convolutional neural networks. However, most of them do not make full use of the context among the hierarchical multi-level features to improve the performance of scene text detection. In this article, we present an efficient multi-level features enhanced cumulative framework based on instance segmentation for scene text detection. At first, we adopt a Multi-Level Features Enhanced Cumulative ( MFEC ) module to capture features of cumulative enhancement of representational ability. Then, a Multi-Level Features Fusion ( MFF ) module is designed to fully integrate both high-level and low-level MFEC features, which can adaptively encode scene text information. To verify the effectiveness of the proposed method, we perform experiments on six public datasets (namely, CTW1500, Total-text, MSRA-TD500, ICDAR2013, ICDAR2015, and MLT2017), and make comparisons with other state-of-the-art methods. Experimental results demonstrate that the proposed Multi-Level Features Enhanced Cumulative Network (MFECN) detector can well handle scene text instances with irregular shapes (i.e., curved, oriented, and horizontal) and achieves better or comparable results.

scholarly journals A Scene Text Detector for Text with Arbitrary Shapes

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Weijia Wu ◽  
Jici Xing ◽  
Cheng Yang ◽  
Yuxing Wang ◽  
Hong Zhou
Keyword(s):  
State Of The Art ◽  
Recognition Task ◽  
Text Detection ◽  
Supplementary Information ◽  
Complex Environment ◽  
Irregular Shapes ◽  
Scene Text Detection ◽  
Scene Text ◽  
F Measure ◽  
Instance Segmentation

The performance of text detection is crucial for the subsequent recognition task. Currently, the accuracy of the text detector still needs further improvement, particularly those with irregular shapes in a complex environment. We propose a pixel-wise method based on instance segmentation for scene text detection. Specifically, a text instance is split into five components: a Text Skeleton and four Directional Pixel Regions, then restoring itself based on these elements and receiving supplementary information from other areas when one fails. Besides, a Confidence Scoring Mechanism is designed to filter characters similar to text instances. Experiments on several challenging benchmarks demonstrate that our method achieves state-of-the-art results in scene text detection with an F-measure of 84.6% on Total-Text and 86.3% on CTW1500.

scholarly journals Natural Scene Text Detection Based On Multi-level Fusion Proposal Network

2020 ◽  
Vol 790 ◽  
pp. 012051
Author(s):  
Tong Li ◽  
Wanggen Li ◽  
Nannan Zhu ◽  
Xuecheng Gong ◽  
Jiajia Chen
Keyword(s):  
Text Detection ◽  
Natural Scene ◽  
Scene Text Detection ◽  
Scene Text ◽  
Multi Level ◽  
Level Fusion

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 MSR: Multi-Scale Shape Regression for Scene Text Detection

2019 ◽  
Author(s):  
Chuhui Xue ◽  
Shijian Lu ◽  
Wei Zhang
Keyword(s):  
Line Length ◽  
Text Detection ◽  
Detection Techniques ◽  
Multi Scale ◽  
The Arts ◽  
Scene Text Detection ◽  
Scene Text ◽  
Scale Network ◽  
Shape Regression ◽  
Public Datasets

State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations.

scholarly journals PSENet-based efficient scene text detection

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guanglong Liao ◽  
Zhongjie Zhu ◽  
Yongqiang Bai ◽  
Tingna Liu ◽  
Zhibo Xie
Keyword(s):  
Feature Extraction ◽  
Text Detection ◽  
Residual Network ◽  
Detection Scheme ◽  
Backbone Network ◽  
Scene Text Detection ◽  
Scene Text ◽  
Text Information ◽  
Computer Vision Applications ◽  
Extract Information

AbstractText detection is a key technique and plays an important role in computer vision applications, but efficient and precise text detection is still challenging. In this paper, an efficient scene text detection scheme is proposed based on the Progressive Scale Expansion Network (PSENet). A Mixed Pooling Module (MPM) is designed to effectively capture the dependence of text information at different distances, where different pooling operations are employed to better extract information of text shape. The backbone network is optimized by combining two extensions of the Residual Network (ResNet), i.e., ResNeXt and Res2Net, to enhance feature extraction effectiveness. Experimental results show that the precision of our scheme is improved more than by 5% compared with the original PSENet.

Multi-oriented scene text detection by fixed-width multi-ratio rotation anchors

2021 ◽  
Vol 95 ◽  
pp. 107428
Author(s):  
Beiji Zou ◽  
Wenjun Yang ◽  
Shu Liu ◽  
Lingzi Jiang
Keyword(s):  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text
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