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SVM Based Hiragana and Katakana Recognition Algorithm with Neural Network Based Segmentation

Published: 29 July 2020 Publication History

Abstract

A Japanese writing system, unlike the European system, is complex. It contains three types of signs: hiragana, katakana and Kanji. For daily use, more than 2000 characters are used, and each symbol can consist of 6 or more strokes. That is why it seems possible to recognise each sign by using a similar approach to fingerprint recognition. Authors are using the minutiae-finding algorithm to find three types of characteristic points. For preprocessing and classification, machine learning algorithms were used. The presented system uses the image of a single sign as an input.

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  • (2021)Hiragana and Katakana Minutiae based Recognition SystemAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0606086:6(54-59)Online publication date: Nov-2021

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  1. SVM Based Hiragana and Katakana Recognition Algorithm with Neural Network Based Segmentation

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      cover image ACM Other conferences
      ICGSP '20: Proceedings of the 4th International Conference on Graphics and Signal Processing
      June 2020
      127 pages
      ISBN:9781450377812
      DOI:10.1145/3406971
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      In-Cooperation

      • University of Macedonia
      • NITech: Nagoya Institute of Technology
      • Zhejiang University: Zhejiang University

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 29 July 2020

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      Author Tags

      1. Handwriting recognition
      2. machine learning
      3. minutiae finding

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      • Refereed limited

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      • partially funded with resources for research by the Ministry of Science and Higher Education in Poland

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      ICGSP 2020

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      • (2021)Hiragana and Katakana Minutiae based Recognition SystemAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0606086:6(54-59)Online publication date: Nov-2021

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