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Javanese Character Recognition Using Hidden Markov Model
Abstract:Hidden Markov Model (HMM) is a stochastic method which has been used in various signal processing and character recognition. This study proposes to use HMM to recognize Javanese characters from a number of different handwritings, whereby HMM is used to optimize the number of state and feature extraction. An 85.7 % accuracy is obtained as the best result in 16-stated vertical model using pure HMM. This initial result is satisfactory for prompting further research.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074467Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1609
 T.E. Behren, A. S─ørat Jatiswara: Struktur dan Perubahan di dalam Puisi Jawa 1600-1930. Jakarta: Indonesian-Netherlands Cooperation in Islamic Studies (INIS), 1995.
 Sam Muharto, and W. Nataatmaja, Trampil Basa Jawa 5: Jilid 5 kangge Kelas V SD/ MI. Solo: PT. Tiga Serangkai Pustaka Mandiri, 2008.
 Roongroj Nopsuwanchai, and Dan Povey, "Discriminative Training for HMM-Based Offline Handwritten Character Recognition". IEEE in the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003).
 Teresa M. Przytycka, Encyclopedia of The Human Genome: Hidden Markov Models. USA: Nature Publishing Group, 2007.
 T. Theeramunkong, C. Wongtapan, and S. Sinthupinyo, "Off-line Isolated Handwritten Thai OCR Using Islandbased Projection with Ngram Models and Hidden Markov Models," IEEE. 2001.
 L. R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceeding of the IEEE, vol 77, pp. 257-286, 1989.
 R. Ngabehi Yasadipura I, Menak China II. Bantanisentrem: Bale Pustaka, 1934.
 R. Ngabehi Yasadipura I, Menak Sorangan. Batavia: Bale Pustaka, 1936.