An Approach to Integrated Pen Interface for Japanese Text Entry

Kazuharu TOYOKAWA; Kozo KITAMURA; Shin KATOH; Hiroshi KANEKO; Nobuyasu ITOH; Masayuki FUJITA

An Approach to Integrated Pen Interface for Japanese Text Entry

Kazuharu TOYOKAWA, Kozo KITAMURA, Shin KATOH, Hiroshi KANEKO, Nobuyasu ITOH, Masayuki FUJITA

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An integrated pen interface system was developed to allow effective Japanese text entry. It consists of sub-systems for handwriting recognition, contextual post-processing, and enhanced Kana-to-Kanji conversion. The recognition sub-system uses a hybrid algorithm consisting of a pattern matcher and a neural network discriminator. Special care was taken to improve the recognition of non-Kanji and simple Kanji characters frequently used in fast data entry. The post-processor predicts consecutive characters on the basis of bigrams modified by the addition of parts of speech and substitution of macro characters for Kanji characters. A Kana-to Kanji conversion method designed for ease of use with a pen interface has also been integrated into the system. In an experiment in which 2,900 samples of Kanji and non-Kanji characters were obtained from 20 subjects, it was observed that the original recognition accuracy of 83.7% (the result obtained by using the pattern matching recognizer) was improved to 90.7% by adding the neural network discriminator, and that it was further improved to 94.4% by adding the post-processor. The improved recognition accuracy for non-Kanji characters was particularly marked.

Publication: IEICE TRANSACTIONS on Information Vol.E77-D No.7 pp.817-824

Publication Date: 1994/07/25

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Type of Manuscript: Special Section PAPER (Special Issue on Document Analysis and Recognition)

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Kazuharu TOYOKAWA, Kozo KITAMURA, Shin KATOH, Hiroshi KANEKO, Nobuyasu ITOH, Masayuki FUJITA, "An Approach to Integrated Pen Interface for Japanese Text Entry" in IEICE TRANSACTIONS on Information, vol. E77-D, no. 7, pp. 817-824, July 1994, doi: .
Abstract: An integrated pen interface system was developed to allow effective Japanese text entry. It consists of sub-systems for handwriting recognition, contextual post-processing, and enhanced Kana-to-Kanji conversion. The recognition sub-system uses a hybrid algorithm consisting of a pattern matcher and a neural network discriminator. Special care was taken to improve the recognition of non-Kanji and simple Kanji characters frequently used in fast data entry. The post-processor predicts consecutive characters on the basis of bigrams modified by the addition of parts of speech and substitution of macro characters for Kanji characters. A Kana-to Kanji conversion method designed for ease of use with a pen interface has also been integrated into the system. In an experiment in which 2,900 samples of Kanji and non-Kanji characters were obtained from 20 subjects, it was observed that the original recognition accuracy of 83.7% (the result obtained by using the pattern matching recognizer) was improved to 90.7% by adding the neural network discriminator, and that it was further improved to 94.4% by adding the post-processor. The improved recognition accuracy for non-Kanji characters was particularly marked.
URL: https://global.ieice.org/en_transactions/information/10.1587/e77-d_7_817/_p

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@ARTICLE{e77-d_7_817,
author={Kazuharu TOYOKAWA, Kozo KITAMURA, Shin KATOH, Hiroshi KANEKO, Nobuyasu ITOH, Masayuki FUJITA, },
journal={IEICE TRANSACTIONS on Information},
title={An Approach to Integrated Pen Interface for Japanese Text Entry},
year={1994},
volume={E77-D},
number={7},
pages={817-824},
abstract={An integrated pen interface system was developed to allow effective Japanese text entry. It consists of sub-systems for handwriting recognition, contextual post-processing, and enhanced Kana-to-Kanji conversion. The recognition sub-system uses a hybrid algorithm consisting of a pattern matcher and a neural network discriminator. Special care was taken to improve the recognition of non-Kanji and simple Kanji characters frequently used in fast data entry. The post-processor predicts consecutive characters on the basis of bigrams modified by the addition of parts of speech and substitution of macro characters for Kanji characters. A Kana-to Kanji conversion method designed for ease of use with a pen interface has also been integrated into the system. In an experiment in which 2,900 samples of Kanji and non-Kanji characters were obtained from 20 subjects, it was observed that the original recognition accuracy of 83.7% (the result obtained by using the pattern matching recognizer) was improved to 90.7% by adding the neural network discriminator, and that it was further improved to 94.4% by adding the post-processor. The improved recognition accuracy for non-Kanji characters was particularly marked.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - An Approach to Integrated Pen Interface for Japanese Text Entry
T2 - IEICE TRANSACTIONS on Information
SP - 817
EP - 824
AU - Kazuharu TOYOKAWA
AU - Kozo KITAMURA
AU - Shin KATOH
AU - Hiroshi KANEKO
AU - Nobuyasu ITOH
AU - Masayuki FUJITA
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 1994
AB - An integrated pen interface system was developed to allow effective Japanese text entry. It consists of sub-systems for handwriting recognition, contextual post-processing, and enhanced Kana-to-Kanji conversion. The recognition sub-system uses a hybrid algorithm consisting of a pattern matcher and a neural network discriminator. Special care was taken to improve the recognition of non-Kanji and simple Kanji characters frequently used in fast data entry. The post-processor predicts consecutive characters on the basis of bigrams modified by the addition of parts of speech and substitution of macro characters for Kanji characters. A Kana-to Kanji conversion method designed for ease of use with a pen interface has also been integrated into the system. In an experiment in which 2,900 samples of Kanji and non-Kanji characters were obtained from 20 subjects, it was observed that the original recognition accuracy of 83.7% (the result obtained by using the pattern matching recognizer) was improved to 90.7% by adding the neural network discriminator, and that it was further improved to 94.4% by adding the post-processor. The improved recognition accuracy for non-Kanji characters was particularly marked.
ER -