Improved Javanese script recognition using custom model of convolution neural network

Abstract

<div align="center"><span lang="EN-US">Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.</span></div>

An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network

“…Convolutional Neural Networks is an architecture that can be trained and consists of several stages [23]. The input to a Convolutional Neural Network is an image, and the process of describing the image into a feature that can be understood by the network is what makes a Convolutional Neural Network different from other neural networks [24], [25].…”