IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 12, No. 3, September 2023, pp. 1448~1458 ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i3.pp1448-1458  1448 Journal homepage: http://ijai.iaescore.com Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation Ajib Susanto 1 , Ibnu Utomo Wahyu Mulyono 1 , Christy Atika Sari 1 , Eko Hari Rachmawanto 1 , De Rosal Ignatius Moses Setiadi 1 , Md Kamruzzaman Sarker 2 1 Department of Informatics Engineering, Dian Nuswantoro University, Semarang, Indonesia 2 Department of Computing Science, University of Hartford, West Hartford, United States Article Info ABSTRACT Article history: Received Oct 17, 2022 Revised Oct 26, 2022 Accepted Dec 21, 2022 Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters. Keywords: Convolution neural network Data augmentation Fourth keyword Javanese script recognition Small dataset This is an open access article under the CC BY-SA license. Corresponding Author: Ajib Susanto Department of Informatics Engineering, Dian Nuswantoro University Semarang, Indonesia Email: ajib.susanto@dsn.dinus.ac.id 1. INTRODUCTION Indonesia is a country comprising numerous ethnic groups and various languages and cultures. One of the largest ethnic groups is the Javanese, who use the Javanese language originally written with the Javanese script. This language is currently rarely used by this ethnicity, therefore it needs to be preserved. Technology- based learning of the Javanese script is one way to re-popularize the writing of this language. This research proposed a highly accurate Javanese script recognition method. Many recognition methods have been proposed. Some are used for Javanese script recognition [1]–[4], as well as non-Latin languages, such as Arabic [5]–[7], Tamil [8], Bangla or Bengali [9]–[11], Kannada [12], Gurmukhi [13], Tifinagh [14], and Thai [15]. Non-Latin character recognition is usually more difficult due to limited research and datasets and the relatively complex shapes of the character. This is also proven in the study by [16] that certain algorithms have better accuracy when interpreting Latin characters than Javanese scripts. Preliminary studies have been carried out on handwritten Javanese script recognition, such as those by [4] and [1]–[3], which are based on machine learning and deep learning, respectively. However, the results obtained are still unsatisfactory because they are limited to basic characters (Carakan). To make a good sentence with Javanese script, the basic (Carakan), vowels (Sandhangan Swara), and consonant scripts (Sandhangan Panyigeg and Sandhangan Wyanjana), including numbers, and punctuation, are required. The vowel, consonant, and basic scripts are used to turn off vocal reading. The vowel and consonant scripts are only used in the middle