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http://dx.doi.org/10.5392/JKCA.2018.18.07.495

Improved Handwritten Hangeul Recognition using Deep Learning based on GoogLenet  

Kim, Hyunwoo (한국외국어대학교 컴퓨터, 전자시스템공학부)
Chung, Yoojin (한국외국어대학교 컴퓨터, 전자시스템공학부)
Publication Information
The Journal of the Korea Contents Association / v.18, no.7, 2018 , pp. 495-502 More about this Journal
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;
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