Alphabet Writing Game Application using Template Matching Cross-correlation

Abstract

The game of writing letters is an attractive learning media. Each person's handwriting is different. So that it requires a data classification method to match the test data with the template that is the alphabet letter. In this journal using a template matching cross-correlation for data classification. Before data classification, preprocessing is done in the form of resize and threshold to produce binary images. Thinning process is also carried out to thin the letters. The thinning algorithm used is stentiford. From the accuracy testing obtained an average value of 70.38%. With the number of letters that continue to experience errors namely the characters H, K, M, and Y.