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

Title:

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

Creator:

Hamplová, Adéla, Franc, David, and Veselý, Arnošt

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:59bd9247-b2ba-42fb-99e4-1d6a1e77c044
uuid:59bd9247-b2ba-42fb-99e4-1d6a1e77c044
doi:10.14311/NNW.2022.32.011

Subject:

artificial intelligence, classification, historical alphabets, mobilenet, and computer vision

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

181-195

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2022 Volume:32 | Number:4

Harvested from:

CDK

Metadata only:

false