Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition

Abstract

The Arabic script is written from right to left and consists of 28 characters, with no capital or lowercase letters. The Arabic script has several orthographic and morphological properties that make handwriting recognition of the Arabic script challenging. In addition, one of the biggest challenges in recognizing Arabic script patterns is the different handwriting styles and characters of each person's writing. The authors propose a study to compare the accuracy of handwriting pattern recognition in Arabic script which has been done previously by comparing five CNN architectures, namely GoogleNet, AlexNet, VGG-16, LeNet-5, and ResNet-50. Considering that previous research has not obtained excellent accuracy. The number of datasets used is 8400 image data and the most optimal comparison of testing and training data is 80:20. Based on the research that has been done, there are several things that the author can conclude. The model is made using 64 filters for each convolution layer because the optimal size is used for 5 architectures, kernel size is 3x3, neurons is 128, dropout weight is 50% to reduce overfitting, learning rate is 0.001, image size is 64x64, the normalization method with the ReLU activation function, and 1-dimensional input image (grayscale), and with a comparison of testing and training data of 80:20. The VGG-16 architectural model is the architecture that gets the highest score, namely 83.99%. This can have good potential to be developed as a medium for learning Arabic script.

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