The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

CNN

Javanese letter

character recognition

Mobile client

2-tier architecture

API

the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.

Harjoseputro, Yulius

Handarkho, Yonathan Dri

Adie, Heronimus Tresy Renata

International Journal of Interactive Mobile Technologies (iJIM); Vol. 13 No. 12 (2019); pp. 67-80

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2019-12-18

Copyright (c) 2019 Yulius Harjoseputro, Heronimus Tresy Renata Adie

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article