DocumentCode :
3462284
Title :
A Study on Japanese Historical Character Recognition Using Modular Neural Networks
Author :
Horiuchi, Tadashi ; Kato, Satoru
Author_Institution :
Dept. of Control Engieering, Matsue Coll. of Technol., Matsue, Japan
fYear :
2009
fDate :
7-9 Dec. 2009
Firstpage :
1507
Lastpage :
1510
Abstract :
It is fundamental work to translate the historical characters called "kuzushi-ji" into the contemporary characters in Japanese historical studies. In this paper, we develop the Japanese historical character recognition system using the directional element features and modular neural networks. Modular neural networks consist of two kinds of classifiers: a rough classifier to find the several candidates of categories for the input pattern, and a set of fine-classifiers that determine the category of the input pattern as the final result of character recognition. We construct the rough-classifier using the self-organizing maps (SOM), which can derive the multi-templates for each category from input data. The fine-classifiers are realized using multilayered neural networks, each of which solves the two-category classification problem. We also use the rough-classifier for the selection the training samples in the learning process of multilayered neural networks in order to reduce the learning time. Through the experiments of historical character recognition for 57 character categories, we confirmed the effectiveness of our proposed method compared with the conventional research.
Keywords :
character recognition; feature extraction; image recognition; learning (artificial intelligence); multilayer perceptrons; pattern classification; self-organising feature maps; Japanese historical character recognition system; directional element features; feature vector extraction; learning process; modular neural networks; multilayered neural networks; rough classifier; self-organizing maps; two-category classification problem; Character recognition; Computer networks; Data preprocessing; Educational institutions; Feature extraction; Multi-layer neural network; Neural networks; Self organizing feature maps; Smoothing methods; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4244-5543-0
Type :
conf
DOI :
10.1109/ICICIC.2009.57
Filename :
5412663
Link To Document :
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