Balinese glyph recognition with gabor filters

Abstract

Abstract Recognizing Balinese glyphs from the Balinese script on palm leaf manuscripts is not trivial. In Balinese script, there are more than a hundred glyphs which represent basic syllables and compound syllables, and also some punctuation marks. They naturally share a strong interclass similarity between each other related to the form of their writing curves. The degraded image of textured palm leaf manuscript also offer some challenging parts in recognizing the Balinese glyph. In this paper, we investigated the use of Gabor filter bank as the feature extraction method to recognize the Balinese glyphs. By using Gabor filter, we can detect many texture variations with different orientations and frequencies. In our experiments, the published dataset of AMADI_LontarSet for glyph recognition was used. It showed a very promising result by using a single hidden layer Neural Network as the classifier. Gabor filters with Zoning method achieved a high enough recognition rate. For future works, Gabor filters will be analyzed in combination with the Histogram of Gradient, Neighborhood Pixel Weight and Kirsch Edges features.