Text Detection on Images using Region-based Convolutional Neural Network

In this paper, a new text detection algorithm that accurately locates picture text with complex backgrounds in natural images is applied. The approach is based primarily on the region-based convolutional neural network anchor system, which takes into account the unique features of the text area, compares it to other object detection tasks, and turns the text area detection task into an object sensing task. Thus, the proposed text to be observed directly in the neural network’s convolutional characteristic map, and it can simultaneously predict the text/non-text score of the proposal and the coordinates of each proposal in the image. Then, we proposed an algorithm for the construction of the text line, to increase the text detection model accuracy and consistency. We found that our text detection operates accurately, even in multiple language detection functions. We also discovered that it meets the 2012 and 2014 International Conference on Document Analysis and Recognition thresholds of 0.86 F-measure and 0.78 F-measure, which clearly shows the consistency of our model. Our approach has been programmed and implemented using Python programming language 3.8.3 for Windows.

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A Detection algorithm based on Convolutional Neural Network

10.20944/preprints201811.0583.v1 ◽

2018 ◽

Author(s):

Fei Rong ◽

Li Shasha ◽

Xu Qingzheng ◽

Liu Kun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Training Model ◽

Test Sample ◽

Training Sample ◽

Detection Algorithm ◽

Small Scale ◽

Detection Model ◽

Sample Data ◽

Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

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A novel scene text detection algorithm based on convolutional neural network

2016 Visual Communications and Image Processing (VCIP) ◽

10.1109/vcip.2016.7805444 ◽

2016 ◽

Cited By ~ 3

Author(s):

Xiaohang Ren ◽

Kai Chen ◽

Xiaokang Yang ◽

Yi Zhou ◽

Jianhua He ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Detection Algorithm ◽

Text Detection ◽

Scene Text Detection ◽

Scene Text

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Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

Scientific Reports ◽

10.1038/s41598-021-01170-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lee Ming Jun Melvin ◽

Rajesh Elara Mohan ◽

Archana Semwal ◽

Povendhan Palanisamy ◽

Karthikeyan Elangovan ◽

...

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Urban Environment ◽

Field Trial ◽

Detection Algorithm ◽

Data Set ◽

Detection Model ◽

Inspection Robot ◽

Online Test

AbstractDrain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called ‘Raptor’. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.

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A Convolutional Neural Network-Based Chinese Text Detection Algorithm via Text Structure Modeling

IEEE Transactions on Multimedia ◽

10.1109/tmm.2016.2625259 ◽

2017 ◽

Vol 19 (3) ◽

pp. 506-518 ◽

Cited By ~ 24

Author(s):

Xiaohang Ren ◽

Yi Zhou ◽

Jianhua He ◽

Kai Chen ◽

Xiaokang Yang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Chinese Text ◽

Detection Algorithm ◽

Text Structure ◽

Text Detection ◽

Structure Modeling

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Text detection algorithm on real scenes images and videos on the base of discrete cosine transform and convolutional neural network

2017 International Siberian Conference on Control and Communications (SIBCON) ◽

10.1109/sibcon.2017.7998591 ◽

2017 ◽

Author(s):

Polina M. Osina ◽

Yuliya A. Bolotova ◽

Vladimir G. Spitsyn

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Discrete Cosine Transform ◽

Detection Algorithm ◽

Text Detection ◽

Cosine Transform

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A Real Time Malaysian Sign Language Detection Algorithm Based on YOLOv3

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1102.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 651-656

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Sign Language ◽

Mobile Application ◽

Detection Algorithm ◽

Hearing Impaired ◽

Impaired Person ◽

Percent Accuracy ◽

Language Detection

Sign language is a language that involves a movement of hand gestures. It is a medium for the hearing impaired person (deaf or mute) to communicate with others. However, in order to communicate with the hearing impaired person, the communicator has to have knowledge in sign language. This is to ensure that the message delivered by the hearing impaired person is understood. This project proposes a real time Malaysian sign language detection based on the Convolutional Neural Network (CNN) technique utilizing the You Only Look Once version 3 (YOLOv3) algorithm. Sign language images from web sources and recorded sign language videos by frames were collected. The images were labelled either alphabets or movements. Once the preprocessing phase was completed, the system was trained and tested on the Darknet framework. The system achieved 63 percent accuracy with learning saturation (overfitting) at 7000 iterations. Once it is successfully conducted, this model will be integrated with other platform in the future such as mobile application.

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Study on Intrusion detection model based on improved convolutional neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1865/4/042097 ◽

2021 ◽

Vol 1865 (4) ◽

pp. 042097

Author(s):

Kaiyan He

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Detection Model ◽

Model Based

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Lightweight Convolutional Neural Network Based Intrusion Detection System

Journal of Communications ◽

10.12720/jcm.15.11.808-817 ◽

2020 ◽

pp. 808-817

Author(s):

Vinh Pham ◽

◽

Eunil Seo ◽

Tai-Myoung Chung

Keyword(s):

Neural Network ◽

Machine Learning ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Network Traffic ◽

Detection System ◽

Image Data ◽

Training Data ◽

Deep Packet Inspection ◽

Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

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