High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization

Yuji WAIZUMI; Nei KATO; Kazuki SARUTA; Yoshiaki NEMOTO

High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization

Yuji WAIZUMI, Nei KATO, Kazuki SARUTA, Yoshiaki NEMOTO

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We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.

Publication: IEICE TRANSACTIONS on Information Vol.E83-D No.6 pp.1282-1290

Publication Date: 2000/06/25

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Type of Manuscript: PAPER

Category: Biocybernetics, Neurocomputing

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Yuji WAIZUMI, Nei KATO, Kazuki SARUTA, Yoshiaki NEMOTO, "High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization" in IEICE TRANSACTIONS on Information, vol. E83-D, no. 6, pp. 1282-1290, June 2000, doi: .
Abstract: We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_6_1282/_p

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@ARTICLE{e83-d_6_1282,
author={Yuji WAIZUMI, Nei KATO, Kazuki SARUTA, Yoshiaki NEMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization},
year={2000},
volume={E83-D},
number={6},
pages={1282-1290},
abstract={We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.},
keywords={},
doi={},
ISSN={},
month={June},}

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TY - JOUR
TI - High Speed and High Accuracy Rough Classification for Handwritten Characters Using Hierarchical Learning Vector Quantization
T2 - IEICE TRANSACTIONS on Information
SP - 1282
EP - 1290
AU - Yuji WAIZUMI
AU - Nei KATO
AU - Kazuki SARUTA
AU - Yoshiaki NEMOTO
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2000
AB - We propose a rough classification system using Hierarchical Learning Vector Quantization (HLVQ) for large scale classification problems which involve many categories. HLVQ of proposed system divides categories hierarchically in the feature space, makes a tree and multiplies the nodes down the hierarchy. The feature space is divided by a few codebook vectors in each layer. The adjacent feature spaces overlap at the borders. HLVQ classification is both speedy and accurate due to the hierarchical architecture and the overlapping technique. In a classification experiment using ETL9B, the largest database of handwritten characters in Japan, (it contains a total of 607,200 samples from 3036 categories) the speed and accuracy of classification by HLVQ was found to be higher than that by Self-Organizing feature Map (SOM) and Learning Vector Quantization methods. We demonstrate that the classification rate of the proposed system which uses multi-codebook vectors for each category under HLVQ can achieve higher speed and accuracy than that of systems which use average vectors.
ER -