Effect of Gradient Descent With Momentum Backpropagation Training Function in Detecting Alphabet Letters

Abstract

The research uses the Momentum Backpropagation Neural Network method to recognize characters from a letter image. But before that, the letter image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimension of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computer systems compared to humans is recognizing character patterns if not using supporting methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In ANN, there are several methods used to train computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train and detect an image is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize the character pattern of handwritten letters well which can make it easier for humans to recognize patterns from letters that are difficult to read due to various error factors seen by humans. The results of the testing process using the Backpropagation algorithm reached 100% with a total of 90 trained data. The test results of the test data reached 100% of the 90 test data.