On Georgian Handwritten Character Recognition

被引：2

作者：

Soselia, Davit ^{[2
]}

Tsintsadze, Magda ^{[1
,2
]}

Shugliashvili, Levan ^{[2
]}

Koberidze, Irakli ^{[2
]}

Amashukeli, Shota ^{[2
]}

Jijavadze, Sandro ^{[2
]}

机构：

[1] Iv Javakhishvili Tbilisi State Univ, Tbilisi, Georgia

[2] San Diego State Univ Georgia, Tbilisi, Georgia

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 30期

关键词：

Artificial intelligence; CNN; SNN; OCR; Handwritten Character Recognition; ONLINE;

D O I：

10.1016/j.ifacol.2018.11.279

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The article addresses the issue of Georgian handwritten text recognition. As a result of the performed research activity, a framework for recognizing handwritten Georgian text using Self Normalizing Convolutional Neural Networks (CNN) was developed. To train the CNN model, an extensive dataset was created with over 200 000 character samples. This framework has been deployed as a web service, as well as in the form of apps for Windows, Linux, and iOS. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：161 / 165

页数：5

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