A Text Detection System for Natural Scenes with Convolutional Feature Learning and Cascaded Classification

2016 ◽  
Author(s):  
Siyu Zhu ◽  
Richard Zanibbi
Keyword(s):  
Detection System ◽  
Feature Learning ◽  
Text Detection ◽  
Natural Scenes

scholarly journals Multilingual Text Detection with Nonlinear Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lin Li ◽  
Shengsheng Yu ◽  
Luo Zhong ◽  
Xiaozhen Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Text Detection ◽  
Natural Scenes ◽  
Nonlinear Network ◽  
Text Feature ◽  
Representational Power ◽  
Multilingual Text

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

A robust arbitrary text detection system for natural scene images

2014 ◽  
Vol 41 (18) ◽  
pp. 8027-8048 ◽  
Author(s):  
Anhar Risnumawan ◽  
Palaiahankote Shivakumara ◽  
Chee Seng Chan ◽  
Chew Lim Tan
Keyword(s):  
Detection System ◽  
Text Detection ◽  
Natural Scene ◽  
Natural Scene Images

scholarly journals Deep Learning Approaches for Intrusion Detection

2021 ◽  
pp. 50-64
Author(s):  
Azar Abid Salih ◽  
Siddeeq Y. Ameen ◽  
Subhi R. M. Zeebaree ◽  
Mohammed A. M. Sadeeq ◽  
Shakir Fattah Kak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Learning ◽  
Detailed Comparison ◽  
Learning Approaches ◽  
Network Intrusion ◽  
Research Problems

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

scholarly journals CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 524
Author(s):  
Yuan Li ◽  
Mayire Ibrayim ◽  
Askar Hamdulla
Keyword(s):  
Information Flow ◽  
Feature Fusion ◽  
Feature Learning ◽  
Text Detection ◽  
Cross Layer ◽  
Scale Feature ◽  
Scene Text Detection ◽  
Scene Text ◽  
Feature Pyramid ◽  
Feature Information

In the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by using rectangular bounding boxes, the previous methods may be insufficient for working with more challenging instances of text. To solve this problem, this paper proposes a scene text detection network based on cross-scale feature fusion (CSFF-Net). The framework is based on the lightweight backbone network Resnet, and the feature learning is enhanced by embedding the depth weighted convolution module (DWCM) while retaining the original feature information extracted by CNN. At the same time, the 3D-Attention module is also introduced to merge the context information of adjacent areas, so as to refine the features in each spatial size. In addition, because the Feature Pyramid Network (FPN) cannot completely solve the interdependence problem by simple element-wise addition to process cross-layer information flow, this paper introduces a Cross-Level Feature Fusion Module (CLFFM) based on FPN, which is called Cross-Level Feature Pyramid Network (Cross-Level FPN). The proposed CLFFM can better handle cross-layer information flow and output detailed feature information, thus improving the accuracy of text region detection. Compared to the original network framework, the framework provides a more advanced performance in detecting text images of complex scenes, and extensive experiments on three challenging datasets validate the realizability of our approach.

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

How Far Deep Learning Systems for Text Detection and Recognition in Natural Scenes are Affected by Occlusion?

2021 ◽  
pp. 198-212
Author(s):  
Aline Geovanna Soares ◽  
Byron Leite Dantas Bezerra ◽  
Estanislau Baptista Lima
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Learning Systems ◽  
Natural Scenes ◽  
Detection And Recognition
Sign in / Sign up

Export Citation Format

Share Document