Agglomerative Clustering of Handwritten Numerals to Determine Similarity of Different Languages

TL;DR

This paper introduces a method to analyze and cluster handwritten numerals from different languages using Siamese networks and agglomerative clustering, revealing regional similarities among languages.

Contribution

It presents a novel approach combining Siamese networks and clustering to measure and analyze language similarities based on handwritten numerals.

Findings

Clusters reveal regional language groupings

Siamese network effectively measures numeral similarity

Method identifies language origins from numeral features

Abstract

Handwritten numerals of different languages have various characteristics. Similarities and dissimilarities of the languages can be measured by analyzing the extracted features of the numerals. Handwritten numeral datasets are available and accessible for many renowned languages of different regions. In this paper, several handwritten numeral datasets of different languages are collected. Then they are used to find the similarity among those written languages through determining and comparing the similitude of each handwritten numerals. This will help to find which languages have the same or adjacent parent language. Firstly, a similarity measure of two numeral images is constructed with a Siamese network. Secondly, the similarity of the numeral datasets is determined with the help of the Siamese network and a new random sample with replacement similarity averaging technique. Finally, an agglomerative clustering is done based on the similarities of each dataset. This clustering technique shows some very interesting properties of the datasets. The property focused in this paper is the regional resemblance of the datasets. By analyzing the clusters, it becomes easy to identify which languages are originated from similar regions.