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A Study on Japanese Historical Character Recognition Using Modular Neural Networks
2009
2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)
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The modular neural networks consist of two kinds of classifiers: a rough-classifier and a set of fine-classifiers. ...
The final result of character recognition is derived by selecting the MLP which has the maximum output among the set of MLPs. ...
Modular Neural Networks for Historical Character Recognition. ...
doi:10.1109/icicic.2009.57
fatcat:gwsamuhmk5ejhiykmuhmgabo6y
Evaluating a Zoning Mechanism and Class-Modular Architecture for Handwritten Characters Recognition
[chapter]
2007
Lecture Notes in Computer Science
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Other set of experiments utilized the IRONOFF database resulting in recognition rates of 89.21% and 80.75% for uppercase and lowercase characters respectively, also with the class-modular MLP. ...
Two architectures were tested and evaluated: a conventional MLP (Multiple Layer Perceptron) and a class-modular MLP. ...
Multi-Layer Perceptron Neural networks (MLP-NN) classifiers have proven to be powerful tools in pattern recognition [14] . ...
doi:10.1007/978-3-540-76725-1_54
fatcat:gfifmuzvsbaqtbscmhixjhqmgy
Dynamic Zoning Selection for Handwritten Character Recognition
[chapter]
2011
Lecture Notes in Computer Science
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The information provided by the first level drives the second level in the selection of the appropriate feature extraction method and the corresponding class-modular neural network. ...
The experimental protocol has shown significant recognition rates for handwritten characters (from 80.82% to 88.13%). ...
-CR)
Table 1 . 1 Conventional NN and Class-Modular MLP-NN Characters
Conventional NN
Recognition Rate (%)
Class-Modular MLP-NN
Recognition Rate (%)
ZGlobal
Z4
Z5H
Z5V
Z7
Z4
Z5H
Z5V
Z7 ...
doi:10.1007/978-3-642-25085-9_60
fatcat:7ygukuq2pvfirawlt5dgrgdoya
The Multi-Class Imbalance Problem: Cost Functions with Modular and Non-Modular Neural Networks
[chapter]
2009
Advances in Soft Computing
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In this paper, the behavior of Modular and Non-Modular Neural Networks trained with the classical backpropagation algorithm in batch mode and applied to classification problems with Multi-Class imbalance ...
So, in our Mod-NN, given a instance test x i , the two class network with the highest rating is taken as the class label for that instance. ...
Multilayer Perceptron (MLP). MLP and RBFNN are two well-known NN in the pattern recognition field [10] . ...
doi:10.1007/978-3-642-01216-7_44
dblp:conf/isnn/AlejoSVC09
fatcat:wfjuoxjnbzaezmjdz2vneujgsy
Combining linear discriminant functions with neural networks for supervised learning
1997
Neural computing & applications (Print)
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The proposed architecture provides an efficient way to apply existing neural networks (e.g. multi-layered perceptron) for solving a large scale problem. ...
For constructive learning, growing and credit-assignment algorithms are developed to serve for the hybrid architecture. ...
We wish to thank Liping Yang for valuable and constructive discussions, as well as for providing a program on the Levenberg-Marquat algorithm for simulation. ...
doi:10.1007/bf01670150
fatcat:s6spr4y7dvgdjil23evjrwwanu
CLASSIFICATION OF SATELLITE IMAGES USING ANN
English
2012
International Journal of Machine Intelligence
English
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Results show that this new neural network model is more accurate than the other NN models. These results suggest that this model is effective for classification of satellite image data. ...
This paper describes the MLP NN classifier performing optimally in classifying the different land types from Landsat data. ...
These networks process their input using several parallel MLPs and then recombine the results. In contrast to the MLP, modular networks do not have full interconnectivity between their layers. ...
doi:10.9735/0975-2927.4.2.414-420
fatcat:ksc2uoapm5fjzawsm4wlw6lnje
From data topology to a modular classifier
2003
International Journal on Document Analysis and Recognition
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Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given. ...
This article describes an approach to designing a distributed and modular neural classifier. ...
Neural networks, and more particularly Multi-Layer Perceptrons (MLP) [8, 15, 33, 34] have received a great deal of attention. ...
doi:10.1007/s10032-002-0095-3
fatcat:ipq45qmnhzbi5cb764tppce4nu
Usage of neural networks in ubiquitous computing systems
2008
2008 Third International Forum on Strategic Technologies
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One of most perspective techniques for sensing in ubiquitous computing systems is neural networks. ...
In this paper we describe features of usage of neural networks in ubiquitous computing and its implementation for solving of some tasks in middleware ubiquitous computing system for smart environment. ...
In our modular neural network we use some multi-layer perceptrons (MLP) for different subsets of visible access points, stored in so-called matrix of visibility obtained during site calibration. ...
doi:10.1109/ifost.2008.4603019
fatcat:n7sd3wz5wbbjrkkmvouacajwpa
IRIS RECOGNITION USING MODULAR NEURAL NETWORK AND FUZZY INFERENCE SYSTEMBASED SCORE LEVEL FUSION
2017
International Journal of Advanced Research in Computer Science
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This paper proposes an approach for recognition of an iris using Modular Neural Network and Fuzzy inference system based Score Level Fusion. ...
Image Acquisition, Feature Extraction, Segmentation and Recognition. ...
architecture of the module used in the present work
consists of five small Multi-Layer Perceptron (MLP) neural
networks. ...
doi:10.26483/ijarcs.v8i7.4260
fatcat:ihko5ofkfjbz3fwkce67l3alpi
A modular classification model for received signal strength based location systems
2008
Neurocomputing
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Previously we reported small scale prototype location estimation system based on modular multi layer perceptron (MLP) networks [2, 1] . ...
Fig. 12 shows an arbitrary structure of an MLP network for location estimation. ...
doi:10.1016/j.neucom.2007.11.045
fatcat:ka47dxu7rjfblp7baaq2voc52e
A review on various techniques used to recognize off-line handwritten Malayalam characters
2021
Malaya Journal of Matematik
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This paper presents a comprehensive survey of various techniques with its accuracy rate on Malayalam off-line handwriting recognition for the past few years. ...
Different feature extraction and classification techniques used in Malayalam off-line handwriting recognition are pointed out. Then databases chosen for these purposes are also discussed. ...
Guru
2007
1D Wavelet
transform
Multi Layer
Perceptron (MLP)
4950 samples of
33 classes.
73.8%
Lajish.V.L
2008
fuzzy-zoned
normalized
vector distance
Class modular
neural network. ...
doi:10.26637/mjm0901/0106
fatcat:af72tejz3rcofpdxky4x56jyrq
In-Situ Learning in Multi-net Systems
[chapter]
2004
Lecture Notes in Computer Science
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We examine collaboration in multi-net systems through in-situ learning, exploring how generalisation can be improved through the simultaneous learning in networks and their combination. ...
Results for these are compared with existing approaches, demonstrating that in-situ trained systems perform better than similar pre-trained systems. ...
For MONK 2, 3 and WBCD, the SLM system out-performs the other single network and multi-net systems. ...
doi:10.1007/978-3-540-28651-6_112
fatcat:dm5nqpqfvzgjjfeydc6kcipxa4
Context-Aware Fuzzy ArtMap for Received Signal Strength Based Location Systems
2007
Neural Networks (IJCNN), International Joint Conference on
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Intrinsically RSS based positioning is a multi-class pattern recognition problem. ...
We present a Context-aware Fuzzy ArtMap neural network that provides competitive location accuracy in comparison with modular approach while leveraging online and incremental learning capabilities to location ...
to Fuzzy ArtMap based location system we implemented two other neural network models for our location system i) Multi Layer Perceptron (MLP) and ii) Learning Vector Quantization (LVQ), in order to evaluate ...
doi:10.1109/ijcnn.2007.4371392
dblp:conf/ijcnn/AhmadGLL07
fatcat:jhgxtww7ozcbrez5qzbaspiifq
Artificial neural networks for document analysis and recognition
2005
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Artificial neural networks have been extensively applied to document analysis and recognition. ...
Index Terms-Character segmentation, document image analysis and recognition, layout analysis, neural networks, preprocessing, recursive neural networks, word recognition. ae . S. Marinai and G. ...
They are also very grateful to the participants of the tutorial Artificial Neural Networks for Document Analysis and ...
doi:10.1109/tpami.2005.4
pmid:15628266
fatcat:oanhch2aufctfd2fly47337j2u
Invariant Object Recognition Using Circular Pairwise Convolutional Networks
[chapter]
2006
Lecture Notes in Computer Science
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We compared the recognition accuracy and training time complexity between our approach and a benchmark generic object recognizer LeNet7 which is a monolithic convolutional network. ...
Out of many techniques, convolutional network (CN) is proven to be a good candidate in this area. ...
To combine these pairwise ratios into a final decision for the multi-class problem, a metaclassifier such as MLP can be trained to map them into their corresponding desired outputs. ...
doi:10.1007/978-3-540-36668-3_167
fatcat:666h5xydjravdhkhdd6xlvl73u
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