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Improved Handwritten Hangeul Recognition using Deep Learning based on GoogLenet

GoogLenet 기반의 딥 러닝을 이용한 향상된 한글 필기체 인식

  • 김현우 (한국외국어대학교 컴퓨터, 전자시스템공학부) ;
  • 정유진 (한국외국어대학교 컴퓨터, 전자시스템공학부)
  • Received : 2018.05.28
  • Accepted : 2018.07.02
  • Published : 2018.07.28

Abstract

The advent of deep learning technology has made rapid progress in handwritten letter recognition in many languages. Handwritten Chinese recognition has improved to 97.2% accuracy while handwritten Japanese recognition approached 99.53% percent accuracy. Hanguel handwritten letters have many similar characters due to the characteristics of Hangeul, so it was difficult to recognize the letters because the number of data was small. In the handwritten Hanguel recognition using Hybrid Learning, it used a low layer model based on lenet and showed 96.34% accuracy in handwritten Hanguel database PE92. In this paper, 98.64% accuracy was obtained by organizing deep CNN (Convolution Neural Network) in handwritten Hangeul recognition. We designed a new network for handwritten Hangeul data based on GoogLenet without using the data augmentation or the multitasking techniques used in Hybrid learning.

Keywords

Handwritten Hangeul Recognition;CNN;GoogLenet;PE92 Database

Acknowledgement

Supported by : 한국외국어대학교, 한국연구재단

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