Improvement On Triangle Features Based Grouping Features for Offline Digit Handwriting

An offline digit handwriting recognition is one of an active studied that has been explored in the field of pattern recognition. In this paper, an improvement on triangle features based grouping features is proposed. It uses to overcome the problem of processing data where the performance is slow based on time training. This problem occurred due to the huge size of the number of triangle features are used. The grouping features are focused on triangle properties of ratio and gradient where the outcome of this grouping features will produce five triangle features which are gRatio-ABC, gGradient-ABC, angle point A, angle point B and angle point C. Then, the converting process using the absolute value function is applied to increase the classification accuracies for digit dataset of IFCHDB, HODA, MNIST and BANGLA. A classifier of Support Vector Machine was used to measure the accuracies

Triangle Geometry Method Based Dominant Distribution Foreground for Digit Recognition

Digit recognition has been studied for four decades ago. Many approaches and techniques such as Hidden Markov Model, Neural Network, back-propagation and k-nearest neighbor have been applied to recognize the digit images. Recently, the triangle geometry method has been applied to extract features from triangle properties such as ratio, angle and gradient. However, a problem in determining points of a triangle was triggered due to the points’ position in straight line. Thus, a method of extracting triangle features using triangle geometry based on the dominant of distribution foreground for digit recognition has been proposed. The dominant of distribution foreground is referred to the digit of ‘0’ which is represented as a foreground image during the binarization process. The process to determine the triangle points are based on the dominant of distribution foreground. The classifiers of Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) are used to measure the classification accuracies for four types of digit datasets which are HODA, IFCHDB, MNIST, and BANGLA. The comparison results classification of accuracies demonstrated the effectiveness of our proposed method