A Novel Framework for Ancient Text Translation Using Artificial Intelligence
Abstract Ancient script has been a repository of knowledge, culture and civilization history. In order to have a greater access to the valuable information present in the ancient scripts, an appropriate translation system needs to be developed keeping complexity and very less knowledge of the script available in consideration. In this study, a translation and prediction system has been implemented using Artificial Intelligence. The training has been developed using Sunda-Dataset and self-generated dataset, whereas the translation from ancient script viz. Sundanese script to English text is done using two layers Recurrent Neural Network. The technique used is compared with an existing translator called IM Translator. The results shows that the BLEU score is increased by 8% in comparison to IM Translator further WER is decreased by 10% in contrast to IM Translator. Furthermore, the N-Gram analysis results indicate 3% to 4% increase in 100% contrast value.
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Al-Muzaini. H. A,. Al-Yahya. T. N, & Benhidour. H, “Automatic Arabic Image Captioning using RNN-LSTM-Based Language Model and CNN,” 2018. [Online]. Available: www.ijacsa.thesai.org.
Apriyanti, T., Wulandari, H., Safitri, M., & Dewi, N. (2016). Translating Theory of English into Indonesian and Vice-Versa. In Indonesian Journal of English Language Studies (Vol. 2, Issue 1).
Athiwaratkun and J. W. Stokes, “Malware classification with lstm and gru language models and a character-level CNN.”, 2016.
Bijalwan, V., Kumar, V., Kumari, P., & Pascual, J. (2014). KNN based machine learning approach for text and document mining. International Journal of Database Theory and Application, 7(1), 61–70. https://doi.org/10.14257/ijdta.2014.7.1.06
Chadha, S., Mittal, S., & Singhal, V. (2019). An insight of script text extraction performance using machine learning techniques. International Journal of Innovative Technology and Exploring Engineering, 9(1), 2581–2588. https://doi.org/10.35940/ijitee.A5224.119119.
Chadha, S., S. Mittal, and V. Singhal. "Ancient text character recognition using deep learning." International Journal of Engineering Research and Technology 3.9 (2020): 2177–2184.
Chaudhary. J and Patel. A, “IJSRSET1844500 | Bilingual Machine Translation Using RNN Based Deep Learning,” vol. 4, 2018, [Online]. Available: www.ijsrset.com.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. https://arxiv.org/abs/1406.1078
Fachrurrozi, M., Yusliani, N., & Agustin, M. M. (n.d.). "Identification of Ambiguous Sentence Pattern in Indonesian Using Shift-Reduce Parsing", 2014
G. Lin and W. Shen, “Research on convolutional neural network based on improved Relu piecewise activation function,” in Procedia Computer Science, 2018, vol. 131, pp. 977–984, https://doi.org/10.1016/j.procs.2018.04.239.
Gautam N. & Chai S. (2020). Translation into Pali Language from Brahmi Script. In: Sharma D.K., Balas V.E., Son L.H., Sharma R., Cengiz K. (eds) Micro-Electronics and Telecommunication Engineering. Lecture Notes in Networks and Systems, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-15-2329-8_12.
Hermanto, A Adji. T, & Setiawan, N (2015). “Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study,” 2015 International Conference on Science in Information Technology (ICSITech), Oct. 2015, doi: https://doi.org/10.1109/icsitech.2015.7407791.
Hinton, G. E., & Zemel, R. S. (n.d.). Autoencoders, Minimum Description Length and Helmholtz Free Energy.
Lauriola, I., Lavelli, A., & Aiolli, F. (2022). An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing, 470, 443–456. https://doi.org/10.1016/j.neucom.2021.05.103.
M. R. Costa-Jussà and J. A. R. Fonollosa, “Character-based Neural Machine Translation,” Mar. 2016, [Online]. Available: https://arxiv.org/abs/1603.00810.
M. Suryani, E. Paulus, S. Hadi, U. A. Darsa, and J. C. Burie, “The Handwritten Sundanese Palm Leaf Manuscript Dataset from 15th Century,” in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, Jul. 2017, vol. 1, pp. 796–800, doi: https://doi.org/10.1109/ICDAR.2017.135.
M. Zhang, Y. Zhang, and D.-T. Vo, “Gated Neural Networks for Targeted Sentiment Analysis.” [Online]. Available: www.aaai.org.
Maitra, D. sen, Bhattacharya, U., & Parui, S. K. (2015). CNN based common approach to handwritten character recognition of multiple scripts. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2015-Novem, 1021–1025. https://doi.org/10.1109/ICDAR.2015.7333916
Markou, K. et al. (2021). A Convolutional Recurrent Neural Network for the Handwritten Text Recognition of Historical Greek Manuscripts. In: , et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_18.
Miyamoto, Y., & Cho, K. (2016). Gated Word-Character Recurrent Language Model. https://arxiv.org/abs/1606.01700.
Nurseitov.D, Bostanbekov.K, Kanatov.M, Alimova.A 1,2 , Abdallah.A, Abdimanap.G (2021). “Classification of handwritten names of cities and Handwritten text recognition using various deep learning models”, Advances in Science, Technology and Engineering Systems Journal Vol. 5.
P. Wang, P. Nakov and H. T. Ng, “Source Language Adaptation Approaches for Resource-Poor Machine Translation,” 2016, doi: https://doi.org/10.1162/COLI.
P. Zhou, Z. Qi, S. Zheng, J. Xu, H. Bao, and B. Xu, “Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling,” Nov. 2016, [Online]. Available: https://arxiv.org/abs/1611.06639.
P.Y. Huang, F. Liu, S.-R. Shiang, J. Oh, and C. Dyer, “Attention-based Multimodal Neural Machine Translation.”, 2018.
Purwarianti.A, Yayat.D, Fakultas.P, “Experiment on a Phrase-Based Statistical Machine Translation Using PoS Tag Information for Sundanese into Indonesian”, International Conference on Information Technology Systems and Innovation (ICITSI), p.p 1–6, 2015.
R. P. Haroon and T. A. Shaharban, “Malayalam machine translation using hybrid approach,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Mar. 2016, doi: https://doi.org/10.1109/iceeot.2016.7754839.
Ray, A., Rajeswar, S., & Chaudhury, S. (2015). Text recognition using deep BLSTM networks. ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition. https://doi.org/10.1109/ICAPR.2015.7050699
Ray, A., Rajeswar, S., & Chaudhury, S. (2015). Text recognition using deep BLSTM networks. ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition. https://doi.org/10.1109/ICAPR.2015.7050699
S. P. Singh, A. Kumar, H. Darbari, L. Singh, A. Rastogi and S. Jain, "Machine translation using deep learning: An overview," 2017 International Conference on Computer, Communications and Electronics (Comptelix), 2017, pp. 162–167, https://doi.org/10.1109/COMPTELIX.2017.8003957. Results should be clear and concise.
Singh. S, Kumar. A, Darbari. H, Singh.L, Rastogi. A & Jain.S, (2017). “Machine translation using deep learning: An overview,” 2017 International Conference on Computer, Communications and Electronics (Comptelix), Jul. 2017, https://doi.org/10.1109/comptelix.2017.8003957.
Swe, T & Tin. P. (2005). Recognition and Translation of the Myanmar Printed Text Based on Hopfield Neural Network. 6th Asia-Pacific Symposium on Information and Telecommunication Technologies, APSITT 2005 - Proceedings. 2005. 99–104. https://doi.org/10.1109/APSITT.2005.203638.
Swe, T., & Tin, P. (n.d.). Recognition and Translation of the Myanmar Printed Text Based on Hopfield Neural Network.
Wicaksono, A and Purwarianti. A, “HMM Based Part-of-Speech Tagger for Bahasa Indonesia Implementing Deep Learning Using Sequence-to-sequence for Automatic Question Generator View project Game refinement theory (M/P) View project HMM Based Part-of-Speech Tagger for Bahasa Indonesia,” 2010. [Online]. Available: https://students.itb.ac.id/home/alfan_fw@students.itb.ac.id/IPOSTAgger.
Windu, M., Kesiman, A., Burie, J.-C., & Ogier, J.-M. (2016). A New Scheme for Text Line and Character Segmentation from Gray Scale Images of Palm Leaf Manuscript. https://doi.org/10.1109/ICFHR.2016.63
Xiaoyuan. Y, Ruoyu. L and Maosong. S, (2017). “Generating Chinese Classical Poems with RNN Encoder-Decoder”, China National Conference on Chinese Computational Linguistics, pp 211–222, 2017.
Verma, S., Gupta, N., B C, A., & Chauhan, R. (2023). A Novel Framework for Ancient Text Translation Using Artificial Intelligence. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 411–425. https://doi.org/10.14201/adcaij.28380
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