A Complete Scheme of Spatially Categorized Glyph Recognition for the Transliteration of Balinese Palm Leaf Manuscripts

Abstract

To open a wider access to the precious content of historical Balinese palm leaf manuscripts, an appropriate system to transliterate the Balinese script to the Roman script is needed. To achieve this goal, a Balinese glyph recognition scheme is very important. This scheme needs to be developed by taking into account the degraded condition of palm leaf manuscripts and the complexity of Balinese script. In this paper, we present a complete scheme of spatially categorized glyph recognition for the transliteration of Balinese palm leaf manuscripts. For this scheme, five different categories of glyph recognizers based on the spatial positions on the manuscript are proposed. These recognizers will be used to verify and to validate the recognition result of the global glyph recognizer. Each glyph recognizer is built based on the combination of some feature extraction methods and it is trained on a single layer neural network. The trained network is initialized by an unsupervised feature learning. The output of the glyph recognition scheme will be sent as the input to the phonological transliteration system. The results are evaluated with the ground truth of transliterated text provided by philologists. Our scheme shows a very promising result for Balinese palm leaf manuscripts transliteration and can be adapted to other type of script.