High speed and high accuracy rough classification for handwritten characters using hierarchical learning vector quantization

Yuji Waizumi, Nei Kato, Kazuki Saruta, Yoshiaki Nemoto

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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 [2], 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) [3] and Learning Vector Quantization [4] 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.

Original languageEnglish
Pages (from-to)1282-1290
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE83-D
Issue number6
Publication statusPublished - 2000 Jan 1

Keywords

  • Hierarchical LVQ
  • LVQ
  • Large scale classification problem
  • Overlapping technique
  • Window

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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