Abstract
The character recognition of cursive scripts always be provocative. The inherent challenges exists in cursive scripts captured researcher’s interest to crop up the issues that surface in building a reliable OCR. There exists many ancient languages that require state of the art techniques to be applied on them. Every such language has its own inherent complex structure. We proposed Balinese character recognition system by Recurrent Neural Network (RNN) approach, so that their characteristics may get substantial attention from research community. The Balinese has Brahmic Indic ancestor having cursive writing style nearest to Devangri, Sinhala and Tamil. We employed BLSTM networks on Balinese character recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prapitasari LPA (2010) Off-line balinese character recognition based on backpropagation neural network. In: Universitas Gunadarma, Proceeding seminar Ilmiah Nasional KOMMIT Nov 2010. ISSN: 1411–6286
Sudarma M, Darma WAS (2014) The identification of Balinese scripts’ characters based on semantic feature and K nearest neighbor. Int J Comp Appl 91(1):0975–8887
Graves A (2012) Offline Arabic handwriting recognition with multidimensional recurrent neural networks. In: Margner V, Abed HE (eds) Guide to OCR for Arabic scripts, ch. 12. Springer, pp 297–231
Hasan A, Ahmed SB, Rashid SF, Shafait F, Breuel TM (2013) Offline printed Urdu Nastaleeq script recognition with bidirectional LSTM networks. In: 12th ICDAR, Aug 2013, pp 1061–1065
Ahmed S, Naz S, Razzak MI, Rashid SF, Afzal MZ, Breuel TM (2015) Evaluation of cursive and non-cursive scripts using recurrent neural networks. Neural Computing Appl 26(3)
Ahmed SB, Naz S, Salahuddin, Razzak MI, Khan AA, Umar AI (2015) UCOM offline dataset—an Urdu handwritten dataset generation. In: International Arab Journal of Information Technology (IAJIT), (in press)
Graves A (2008) Supervised sequence labelling with recurrent neural networks. Ph.D. dissertation, Technical University Munich
Graves A (2012) Supervised sequence labelling with recurrent neural networks ser. In: Studies in comput intelligence. vol 385, Springer
Hochreiter S, Schmidhuber J (1997) Long short term memory. In: Neural computation
Graves A, Fern S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural nets. In: ICML
Naz S, Hayat K, Razzak MI, Khan SU, Anwar MW, Madani SA (2014) The optical character recognition of Urdu-like cursive scripts. Pattern Recognit 47(3)
Naz SA, Umar AI, Shirazi SH, Ahmed SB, Razzak MI, Siddiqi I (2015) A review of segmentation techniques for recognition of Arabic-like scripts. Education and Information Technologies 20(2), Springer
Acknowledgment
We would like to say thanks to Mr. Luh Prapitasar and all contributors for providing us Balinese handwritten text samples.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ahmed, S.B., Naz, S., Razzak, M.I., Yusof, R., Breuel, T.M. (2016). Balinese Character Recognition Using Bidirectional LSTM Classifier. In: Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture Notes in Electrical Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-32213-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-32213-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32212-4
Online ISBN: 978-3-319-32213-1
eBook Packages: EngineeringEngineering (R0)