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The Application of Convolution Neural Networks in Handwritten Numeral Recognition
2015
International Journal of Database Theory and Application
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They have been applied to many image recognition tasks and have attracted the attention of the researchers of many countries in recent years. ...
Convolutional neural networks are a technology that combines artificial neural networks and recent deep learning methods. ...
Acknowledgements This work is supported by the National Natural Science Foundations of China(61402265 and 61170054 ) ...
doi:10.14257/ijdta.2015.8.3.32
fatcat:5t565xg6vze3dkp62prjhehz64
Image Pre-processing on Character Recognition using Neural Networks
2013
International Journal of Computer Applications
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The use of artificial neural network in applications can dramatically simplify the code and improve quality of recognition while achieving good performance. ...
This paper, presents a theoretical and practical basis of preprocessing on handwritten text for character recognition using forward-feed neural networks. ...
Back Propagation Algorithm The feed forward network can be applied to a variety of classification and recognition problems. ...
doi:10.5120/14175-2165
fatcat:o7bhyxubcfcdvbk5xexa6qv2wi
Novel Optimization of Identified Palm Geometry Using Image Segmentation
2022
International Journal of Online and Biomedical Engineering (iJOE)
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Further proposed system uses machine learning approach (convolution neural network and Siamese Neural Network) to further assist in optimizing the segmentation performance. ...
a case study of finger recognition. ...
in order to construct an input mask followed by applying Siamese Neural Network. ...
doi:10.3991/ijoe.v18i05.29361
fatcat:dhg7f5vdt5bupes5xaxvmrrx5m
Recognition of Off-Line Hand-Written Alphabets Using Knowledge-Based Computational Intelligence
2019
Pakistan journal of engineering & technology
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A system is proposed to recognize characters using convolutional neural network and is evaluated on a benchmark dataset named as EMNIST to show the performance of the proposed technique. ...
This work provides such a simplified and accurate method for the recognition of handwritten characters. ...
NEURAL NETWORKS A. Neural Networks' Background Neural networks try to solve the problem with a different approach inspired from human visual cortex. ...
doaj:88f00dccc7a943abb557baaaf3d003d7
fatcat:kanhuncw2nfgho3srmdo6ta3hm
A Bayesian approach for initialization of weights in backpropagation neural net with application to character recognition
[article]
2020
arXiv
pre-print
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In this paper, an original algorithm for initialization of weights in backpropagation neural net is presented with application to character recognition. ...
Convergence rate of training algorithms for neural networks is heavily affected by initialization of weights. ...
Special thanks go to Dott. Tiziana Armano and Prof. Anna Capietto for their support to this work. We would like to thanks the anonymous referees whose suggestions have improved the paper. ...
arXiv:2004.01875v1
fatcat:z235l2kzmzhtvpbhgqwnddl4ne
A Bayesian approach for initialization of weights in backpropagation neural net with application to character recognition
2016
Neurocomputing
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In this paper, an original algorithm for initialization of weights in backpropagation neural net is presented with application to character recognition. ...
Convergence rate of training algorithms for neural networks is heavily affected by initialization of weights. ...
Since neural nets are applied to many different complex problems, these methods have fluctuating performances. ...
doi:10.1016/j.neucom.2016.01.063
fatcat:i5ty5t335rb3paw7giqpuqlmoq
FPGA-based Implementation of Stochastic Configuration Network for Robotic Grasping Recognition
2020
IEEE Access
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Compared with the traditional back propagation (BP) neural network [10] , it has better performance in terms of time and accuracy. ...
Therefore, in some applications that require real-time performance, compared with software-based neural networks, FPGA-based neural networks will have more advantages due to the parallel processing characteristics ...
doi:10.1109/access.2020.3012819
fatcat:hti2j6m62jd6hhxd3jwwfpfmcm
Discriminative training for speech recognition
1992
Journal of the Acoustical Society of Japan (E)
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The recent advent of artificial neural networks (ANN) and learning vector quantization (LVQ), and their good performance in speech recognition tasks, has revived an interest in the discriminative training ...
The 1991 IEEE Workshop on Neural Networks for Signal Processing held in Princeton, New Jersey was a good occasion to discuss and recognize the im portance of theoretical advancements in discrimina tive ...
The resulting ANNs were applied to phoneme recognition with an improvement of recognition performance. ...
doi:10.1250/ast.13.331
fatcat:gm65fwyk4bfojlvsf5ltw2c6mi
COMPARATIVE CHARACTERISTICS OF KERAS AND LASAGNE MACHINE LEARNING PACKAGES
2017
Electronics and Control Systems
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A comparative analysis of the Lasagne and Keras computer libraries has been performed to construct convolutional neural networks used in image processing systems. ...
The carried out researches have allowed to define their advantages and disadvantages that will allow to make to researchers the correct choice at the decision of applied problems. ...
A natural analog shows that the set of problems that are not yet subject to the resolution of existing computers, but can be effectively solved by convulated neural networks [1] .
II. ...
doi:10.18372/1990-5548.53.12151
fatcat:es6ufddqjfa3tnmm3hqxq5mxme
Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems
[article]
2019
arXiv
pre-print
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By employing three datasets, we test the use of a population of single neuron networks in performing multi-class recognition tasks. ...
We investigate the ability to model high dimensional recognition problems using single or several neurons networks that are relatively easier to train. ...
Since over-reduction of network size alone, shows poor performance in multi-class recognition problems as it appears in Fig. 1(g) , simplifying the recognition problem might be the solution. ...
arXiv:1905.12135v1
fatcat:qxmnrtzwgfbetabhlgaspdsztq
A Novel Combinational Convolutional Neural Network for Automatic Food-Ingredient Classification
2019
Computers Materials & Continua
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With the development of deep learning and Convolutional Neural Networks (CNNs), the accuracy of automatic food recognition based on visual data have significantly improved. ...
We construct an up-to-date combinational convolutional neural network (CBNet) with a subnet merging technique. Firstly, two different neural networks are utilized for learning interested features. ...
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study. ...
doi:10.32604/cmc.2020.06508
fatcat:p5kdg736hvcvjjih4zfa6scpsy
Deep Convolutional Neural Network for Recognizing the Images of Text Documents
2019
Modern Machine Learning Technologies
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A comparative analysis of various methods and architectures used to solve the problem of object detection is carried out. ...
A neural network algorithm for labeling images in text documents is developed on the basis of image preprocessing that simplifies the localization of individual parts of a document and the subsequent recognition ...
The algorithm simplifies the localization of individual parts of a document and the subsequent recognition of localized blocks using a deep convolutional neural network. ...
dblp:conf/momlet/GolovkoKMKSBS19
fatcat:mg6vpxpnwncjpckswpondwxgvm
Character Recognition of Vehicle License Plate using Feature Extraction: A Review Paper
2018
International Journal for Research in Applied Science and Engineering Technology
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The License Plate Recognition (LPR) system is the most popular method for the mass surveillance of the automobiles now days. ...
The LPR system is basically works into 3 steps , 1 st step is to recognize vehicle license plate and then extract numbers from that and third step is to character recognition means to read out the characters ...
Now a day's , various types of multilayer NN are being applied for the character recognition eg. ...
doi:10.22214/ijraset.2018.3514
fatcat:hjxyfd6nk5bijeskyg6eo3pdoy
Residual Learning Based CNN for Gesture Recognition in Robot Interaction
2021
Journal of Information Processing Systems
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Hence, a residual learning neural network based on a deep convolutional neural network is proposed. First, small convolution kernels are used to extract the local details of gesture images. ...
The complexity of deep learning models affects the real-time performance of gesture recognition, thereby limiting the application of gesture recognition algorithms in actual scenarios. ...
This network overcomes the problem of excessive deep network parameters to a certain extent and reduces the possibility of gradient dispersion problems. ...
doi:10.3745/jips.01.0072
dblp:journals/jips/Han21
fatcat:duqwer27gnfsrcdbzlcdr7kxy4
Automatic Temporal Location and Classification of Human Actions Based on Optical Features
2009
2009 2nd International Congress on Image and Signal Processing
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neural networks. ...
This paper presents a method for automatic temporal location and recognition of human actions. The data are obtained from a motion capture system. ...
ACKNOWLEDGMENT The authors would like acknowledge Paul Slinger for his aid through the course of this research. ...
doi:10.1109/cisp.2009.5304683
fatcat:5wxq3yv4qbfnrbxvn5vnhh4wmu
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