Abstract
In this paper, a statistical method using a novel feature set is developed to identify ancient script, which is moderately difficult due to a lack of metrics with theoretical reinforcements. To analyze changes in the structure of character that contains complexity, especially in shapes, an efficient computable framework is needed. The machine takes the Brahmi picture as input and converts it into a modern Tamil digital format from the twentieth century. Based on the shape and writing pattern, features are extracted for study. The function vectors are configured using the feature that is present at the CNN frameworkâs ultimate layer. To predict Tamil characters, multitask learning is applied to feature vectors.
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
Similar content being viewed by others
References
Li, Y., Zheng, Y., Doermann, D., Jaeger, S.: Script-independent text line segmentation in freestyle handwritten documents. IEEE Trans. Pattern Anal. Mach. Intell. 30(8), 1313â1329 (2008)
Rao, R.P.: Probabilistic analysis of an ancient undeciphered script. IEEE Comput. 43(4), 76â80 (2010)
Altmann, G., & Fengxiang, F. (eds.).: Analyses of Script: Properties of Characters and Writing Systems, vol. 63. Walter de Gruyter (2008)
Changizi, M.A., Zhang, Q., Ye, H., Shimojo, S.: The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes. Am. Nat. 167(5), E117âE139 (2006)
Zaghden, N., Mullot, R., Alimi, A.M.: Characterization of ancient document images composed by Arabic and Latin scripts. In: 2011 International Conference on Innovations in Information Technology, pp. 124â127. IEEE, April (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Âİ 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anilkumar, C., Karrothu, A., Aishwaryalakshmi, G. (2022). Framework for Statistical Machine Recognition and Translation of Ancient Tamil Script. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 2. Smart Innovation, Systems and Technologies, vol 283. Springer, Singapore. https://doi.org/10.1007/978-981-16-9705-0_50
Download citation
DOI: https://doi.org/10.1007/978-981-16-9705-0_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9704-3
Online ISBN: 978-981-16-9705-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)