scholarly journals The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

2019 ◽  
Vol 13 (12) ◽  
pp. 67
Author(s):  
Yulius Harjoseputro ◽  
Yonathan Dri Handarkho ◽  
Heronimus Tresy Renata Adie
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Rapid Development ◽  
Mobile App ◽  
Convolution Neural Network ◽  
Mobile Technologies ◽  
Mobile Client ◽  
Computation Cost ◽  
Program Interface ◽  
Server Architecture

<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>

scholarly journals A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250782
Author(s):  
Bin Wang ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Semantic Features ◽  
General Object ◽  
High Level ◽  
The Impact

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

scholarly journals Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network

2021 ◽  
Author(s):  
Yuguang Ye
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Sparse Representation ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Response Speed ◽  
Gaussian Mixture ◽  
Image Features ◽  
Convolution Neural Network ◽  
Processing Efficiency

Abstract With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

scholarly journals INDOOR POSITIONING USING CONVOLUTION NEURAL NETWORK TO REGRESS CAMERA POSE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1289-1294 ◽  
Author(s):  
J.-M. Ciou ◽  
E. H.-C. Lu
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Rapid Development ◽  
Effective Means ◽  
Recognition Rate ◽  
Indoor Positioning ◽  
Convolution Neural Network ◽  
Human Vision ◽  
Indoor Environments ◽  
Camera Position

<p><strong>Abstract.</strong> In recent years, the issue of indoor positioning has become more and more popular and attracted more attention. Under the absence of GNSS, how to more accurately position is one of the challenges on the positioning technology. Camera positioning can be calculated by image and objects. Therefore, this study focuses on locating the user's camera position, but how to calculate the camera position efficiently is a very challenging problem. With the rapid development of neural network in image recognition, computer can not only process images quickly, but also achieve good results. Convolution Neural Network (CNN) can sense the local area of the image and find some high-resolution local features. These basic features are likely to form the basis of human vision and become an effective means to improve the recognition rate. We use a 23-layer convolutional neural network architecture and set different sizes of input images to train the end-to-end task of location recognition to regress the camera's position and direction. We choose the sites where are the underground parking lot for the experiment. Compared with other indoor environments such as chess, office and kitchen, the condition of this place is very severe. Therefore, how to design algorithms to train and exclude dynamic objects using neural networks is very exploratory. The experimental results show that our proposed solution can effectively reduce the error of indoor positioning.</p>

scholarly journals Handwritten Tamil Character Recognition Using Convolution Neural Network by Adam Optimizer

2021 ◽  
pp. 40-45
Author(s):  
Mrs. R. Iyswarya ◽  
S. Deepak ◽  
P. Jagathratchagan ◽  
Jai Kailash
Keyword(s):  
Neural Network ◽  
Performance Measures ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Convolution Neural Network ◽  
Handwritten Character Recognition ◽  
Considerable Research ◽  
Optical Character ◽  
Electronic Method ◽  
And Performance

Optical Character Recognition is a widely used electronic method for recognition of handwritten images. Tamil handwritten character recognition is complex to recognize. Hence considerable research efforts have been taken in this field. The complexities of writers and the characters, structure over looping and unwanted character portions are the major challenges faced in Tamil characters. RGB to grayscale conversion, image complementation and structure morphing are enclosed in the preprocessing phase. The processed images are subject to recognition with optimized CNN. The connected layers are enhanced using ADAM optimizer for improvement of the standard. The accuracy and performance of the proposed work is compared with other models with certain performance measures.

scholarly journals Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 185-192
Author(s):  
Feng Shan ◽  
◽  
Hui Sun ◽  
Xiaoyun Tang ◽  
Weiwei Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Rate ◽  
Test Site ◽  
Recognition System ◽  
Image Features ◽  
Convolution Neural Network ◽  
Digital Display ◽  
Industrial Control ◽  
Intelligent Recognition

Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.

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