Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation

Ajib Susanto, Ibnu Utomo Wahyu Mulyono, Christy Atika Sari, Eko Hari Rachmawanto, De Rosal Ignatius Moses Setiadi, Md Kamruzzaman Sarker


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.


Convolution neural network; Data augmentation; Fourth keyword; Javanese script recognition; Small dataset

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DOI: http://doi.org/10.11591/ijai.v12.i3.pp1448-1458


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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