An improved classifier and transliterator of hand-written Palmyrene letters to Latin

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F22%3A94074" target="_blank" >RIV/60460709:41110/22:94074 - isvavai.cz</a>

Výsledek na webu

<a href="http://nnw.cz/obsahy22.html" target="_blank" >http://nnw.cz/obsahy22.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2022.32.011" target="_blank" >10.14311/NNW.2022.32.011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An improved classifier and transliterator of hand-written Palmyrene letters to Latin

Popis výsledku v původním jazyce

This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0,6893 to 0,9821 by 142 % for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.

Název v anglickém jazyce

An improved classifier and transliterator of hand-written Palmyrene letters to Latin

Popis výsledku anglicky

This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0,6893 to 0,9821 by 142 % for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

1210-0552

Svazek periodika

32

Číslo periodika v rámci svazku

4 2022

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

15

Strana od-do

181-195

Kód UT WoS článku

000912363100001

EID výsledku v databázi Scopus

2-s2.0-85147326925