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Preservation of Hanacaraka Characters in Old Manuscripts Using Machine Learning Approach

Satiabudhi, Gregorius and INDRAYANA, HANS CHRISTIAN and Liliana, and Yulia, and Adipranata, Rudy (2018) Preservation of Hanacaraka Characters in Old Manuscripts Using Machine Learning Approach. In: 5th International Conference on Communication and Computer Engineering, 19-07-2018 - 19-07-2018, Malacca - Malaysia.

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    Abstract

    The Javanese language has a unique set of the letters called Hanacaraka characters, which is different compared to Latin alphabet. Since modern Javanese ethnics of Indonesia don’t use it anymore for formal conversation and education, this language, especially its Hanacaraka characters begins to extinct. For the preservation purpose of old manuscripts in Hanacaraka characters, we create a system that can recognise Javanese characters automatically from old manuscript or writing. For this system, we investigated and employed several methods of image processing, features extractions and machine learning for character recognizer. In this paper, we present the result of our investigation of traditional feed-forward neural networks and Elman recurrent networks and comparing their accuracies to obtain the best recognizer. We also compare the results with the accuracies of probabilistic neural network and induction tree from our previous experiments. From the comparison we found that Elman recurrent network outperforms the performance of other algorithms, with accuracy more than 97% for data training and 85% for data testing.

    Item Type: Conference or Workshop Item (Paper)
    Uncontrolled Keywords: Cultural preservation; Elman recurrent networks; Feed-forward networks; Hanacaraka characters; Machine learning
    Subjects: Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Industrial Technology > Informatics Engineering Department
    Depositing User: Admin
    Date Deposited: 25 Aug 2018 07:06
    Last Modified: 03 Sep 2018 16:14
    URI: http://repository.petra.ac.id/id/eprint/17931

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