Offline Handwritten Digit Recognition Using Triangle Geometry Properties

Abstract

Offline digit handwritten recognition is one of the frequent studies that is being explored nowadays. Most of the digit characters have their own handwriting nature. Recognizing their patterns and types is a challenging task to do. Lately, triangle geometry nature has been adapted to identify the pattern and type of digit handwriting. However, a huge size of generated triangle features and data has caused slow performances and longer processing time. Therefore, in this paper, we proposed an improvement on triangle features by combining the ratio and gradient features respectively in order to overcome the problem. There are four types of datasets used in the experiment which are IFCHDB, HODA, MNIST and BANGLA. In this experiment, the comparison was made based on the training time for each dataset Besides, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) techniques are used to measure the accuracies for each of datasets in this study.