A Period Prediction System for Sinhala Epigraphical Scripts using Ensemble CNNs and Attention Modules
Article Sidebar
Main Article Content
Abstract
Identifying the period of epigraphical scripts is crucial for archaeologists and others to determine the age of inscriptions. Since different sets and shapes of letters were used in different eras, identifying the period of an epigraphical script also aids in recognizing these scripts. An ideal period prediction system should detect the era of an epigraphical script in real time with high accuracy. The objective of this study is to develop an automated system to predict the period of Sri Lankan Sinhala epigraphical scripts. In the first stage, a dataset of Sinhala epigraphical letter images was created using 7,012 samples from estampage pictures of Sri Lankan inscriptions, addressing the absence of a proper dataset. The proposed approach is more efficient than previous models as it can detect the period of individual letters as well as the period of raw, whole estampage images. Moreover, the approach incorporates a mechanism to detect the period of letters from inscriptions written between two consecutive eras. An ensemble CNN model with attention modules is utilized to identify the eras of epigraphical scripts. Experimental results show that the proposed system achieves an average classification accuracy of 93.88% in identifying the era of individual letters. The system can automatically determine the era of an inscription by analyzing its estampage image within thirty seconds.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
P. Subashini, M. Krishnaveni and N. Sridevi, “Period Prediction System for Tamil Epigraphical Scripts Based on Support Vector Machine,” International Conference on Information Systems for Indian Languages, pp. 23–30, 2011.
S. V. K. Kumar and T. V. Poornima, “An Efficient Period Prediction System for Tamil Epigraphical Scripts Using Transductive Support Vector Machine,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 9, pp. 7999–8002, Sep. 2014.
B. Gangamma, K. S. Murthy and P. Punitha, “Curvelet Transform based approach for prediction of epigraphical scripts era,” 2012 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India, pp. 1-6, 2012.
S. S. Bhat and H. V. B. Achar, “Character recognition and Period prediction of ancient Kannada Epigraphical scripts,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 3, no. 1, pp. 114–118, Feb. 2016,.
S Anvesh and G. H. Kumar, “SVM Classifier for the Prediction of Era of an Epigraphical Script,” International Journal of Peer to Peer Networks, vol. 2, no. 2, pp. 12–22, Apr. 2011.
A. Soumya and G. H. Kumar, “Classification of Ancient Epigraphs into Different Periods Using Random Forests,” 2014 Fifth International Conference on Signal and Image Processing, Bangalore, India, pp. 171-178, 2014.
P. Bannigidad and C. Gudada, “Identification and classification of historical Kannada hand-written document images using LBP features,” International Journal of Intelligent Systems Design and Computing, vol. 2, no. 2, p. 176, 2018.
P. Bannigidad and C. Gudada, “Age-Type Identification and Recognition of Historical Kannada Handwritten Document Images Using HOG Feature Descriptors,” Computing, Communication and Signal Processing, pp. 1001–1010, 2019.
P. S. M et al., “CNN Based Character Recognition and Classification in Tamil Palm Leaf Manuscripts,” 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, pp. 1-6, 2022.
S. Klassen, J. Weed, and D. Evans, “Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia,” PLOS ONE, vol. 13, no. 11, p.e0205649, Nov. 2018.
H. S. Mohana and B. K. Rajithkumar, “Era identification and recognition of Ganga and Hoysala phase Kannada stone inscriptions characters using advance recognition algorithm,” 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India, pp. 659-665, 2014.
W. Geiger, “The trustworthiness of the Mahavamsa,” in The Indian Historical Quarterly, pp. 205–228, 1930.
M. Dias, Lakdiwa sellipiwalin heliwana Sinhala bhashawe prathyartha namayange vikashanaya, Colombo, Sri Lanka: Department of Archaeology, Sri Lanka, 1996.
Bandara, Warnajith, Minato, and Ozawa, “Creation of precise alphabet fonts of early Brahmi script from photographic data of ancient Sri Lankan inscriptions,” Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, vol. 3, no. 3, pp. 33–39, May 2012.
H. M. S. C. R. Heenkenda and T. G. I. Fernando, “Approaches Used to Recognise and Decipher Ancient Inscriptions: A Review,” Vidyodaya Journal of Science, vol. 23, no. 2, 2020.
R. Salomon, Indian epigraphy: a guide to the study of inscriptions in Sanskrit, Prakrit, and the other Indo-Aryan languages, Oxford University Press, 1998.
R. A. E. Coningham, F. R. Allchin, C. M. Batt, and D. Lucy, “Passage to India? Anuradhapura and the Early Use of the Brahmi Script,” Cambridge Archaeological Journal, vol. 6, no. 1, pp. 73–97, Apr. 1996.
P. E. E. Fernando, “Palaeographical Development of the Brahmi Script in Ceylon from 3rd Century BC to 7th Century AD,” University of Ceylon Review, vol. 4, pp. 232–301, 1949.
S. T. Nandasara and Y. Mikami, “Bridging the A Period Prediction System for Sinhala Epigraphical Scripts using Ensemble CNNs and Attention Modules digital divide in Sri Lanka: some challenges and opportunities in using Sinhala in ICT,” International Journal on Advances in ICT for Emerging Regions (ICTer), vol. 8, no. 1, p. 1, Mar. 2016.
J. B. Disanayaka, The structure of spoken Sinhala: Sounds and their patterns, National Institute of Education, Sri Lanka, 1991.
S. M. Aswatha, A. N. Talla, J. Mukhopadhyay, and P. Bhowmick, “A Method for Extracting Text from Stone Inscriptions Using Character Spotting,” Asian Conference on Computer Vision, pp. 598–611, 2014.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, Dec. 2012.
X. Fu, Z. Yang, Z. Zeng, Y. Zhang, and Q. Zhou, “Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective,” ISPRS International Journal of GeoInformation, vol. 11, no. 1, p. 45, Jan. 2022.
K.G.N.D. Karunarathne, K.V. Liyanage, D.A.S. Ruwanmini, G.K.A. Dias and S.T. Nandasara, “Recognizing ancient Sinhala inscription characters using neural network technologies,” International Journal of Scientific Engineering and Applied Science, vol. 3, no. 1, Jan. 2017.
K. A. S. A. Nilupuli Wijerathna et al., “Recognition and translation of Ancient Brahmi Letters using deep learning and NLP,” 2019 International Conference on Advancements in Computing (ICAC), Malabe, Sri Lanka, pp. 226-231, 2019.
S. Pabasara and T. Kokul, “Period Prediction of Sinhala Epigraphical Scripts using Convolutional Neural Networks,” 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter), Colombo, Sri Lanka, pp. 141-146, 2021.
N. Warnajith, “Computer Analysis of Photographic Data of Sri Lankan Early Brahmi Inscriptions,” IOSR Journal of Engineering, vol. 3, no. 01, pp. 44–49, Jan. 2013.
D. A. S. Ruwanmini, K. V. Liyanage, K. G. N. D. Karunarathne, K. A. Dias and S. T. Nandasara, “An architecture for an inscription recognition system for Sinhala epigraphy,” International Journal of Research - GRANTHAALAYAH, vol. 4, no. 12, pp. 48–64, Dec. 2016.
S. Paranavitana, “Inscriptions of Ceylon,” Department of Archaeology, Sri Lanka, vol. 1, 1970.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Computer Vision and Pattern Recognition, Sep. 2014.
D. M. d. Z. Wickremasinghe et al., “Epigraphia Zeylanica,” Government of Ceylon, vol. 5, 1956. 567
Z. Wickremasinghe, “A Sinhalese inscription of 1745 A.D.,” in Bijdragen Tot de Taal-, Land En Volkenkunde van Nederlandsch-Indi¨e, pp. 659–661, 1894.
A. Buades, B. Coll and J.-M. Morel, “Non-Local Means Denoising,” Image Processing on Line, vol. 1, pp. 208–212, Sep. 2011.
P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for Activation Functions,” arXiv: Neural and Evolutionary Computing, Oct. 2017.
F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800-1807, 2017.
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell and S. Xie, “A ConvNet for the 2020s,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2022.
H. M. S. C. R. Heenkenda and T. G. I. Fernando, “Chronological attribution of Sinhalese inscriptions using deep learning approaches,” Journal of the National Science Foundation of Sri Lanka/Journal of the National Science Foundation, vol. 51, no. 3, Oct. 2023.
S. Ruwanmini, K. Dias, C. Niluckshini and T. Nandasara, “Sinhala Inscription Character Recognition Model using Deep Learning Technologies,” International Journal on Advances in ICT for Emerging Regions, vol. 16, no. 1, pp. 1–11, Jun. 2023.