Deciphering Ancient Tamil Epigraphy: A Deep Learning Approach for Vatteluttu Script Recognition

Vatteluttu (Vattezhuthu) script, prevalent between the 3rd and 8th century CE, played a crucial role in documenting early Chola and Pandya history. However, despite its historical significance, thousands of inscriptions remain untranslated, hindering a comprehensive understanding of early South Indian heritage. This research addresses these challenges by developing a deep learning-based approach for digitizing and recognizing Vatteluttu characters. Using a dataset of 1,800 segmented images representing 28 characters, the study integrates advanced preprocessing techniques and a Siamese CNN-RNN architecture to classify ancient scripts with an overall accuracy of 98%. The findings demonstrate the feasibility of automated transcription for ancient scripts, offering a robust framework for preserving and enhancing research on South Indian heritage.

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Publication Details

Journal
Journal of Internet Services and Information Security
Published
2025-02-28
DOI
https://doi.org/10.58346/jisis.2025.i1.030
Citations
3
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
3.83
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article

Deciphering Ancient Tamil Epigraphy: A Deep Learning Approach for Vatteluttu Script Recognition

Manjunath G. Asuti, Venkatesh Bhandage, Praveen S. Challagidad, Nirmalkumar S. Benni et al.
3 citations
Journal of Internet Services and Information Security
Handwritten Text Recognition Techniques
3.83
article

Deciphering Ancient Tamil Epigraphy: A Deep Learning Approach for Vatteluttu Script Recognition

Manjunath G. Asuti, Venkatesh Bhandage, Praveen S. Challagidad, Nirmalkumar S. Benni, Ravi Shankar, Nijaguna Gollara Siddappa, R. Vijaya Arjunan
article en
3 citations

Abstract

Vatteluttu (Vattezhuthu) script, prevalent between the 3rd and 8th century CE, played a crucial role in documenting early Chola and Pandya history. However, despite its historical significance, thousands of inscriptions remain untranslated, hindering a comprehensive understanding of early South Indian heritage. This research addresses these challenges by developing a deep learning-based approach for digitizing and recognizing Vatteluttu characters. Using a dataset of 1,800 segmented images representing 28 characters, the study integrates advanced preprocessing techniques and a Siamese CNN-RNN architecture to classify ancient scripts with an overall accuracy of 98%. The findings demonstrate the feasibility of automated transcription for ancient scripts, offering a robust framework for preserving and enhancing research on South Indian heritage.

Journal of Internet Services and Information SecurityVol. 15(1)
Openalex Percentile: Top 7%
Handwritten Text Recognition Techniques
3.83
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Deciphering Ancient Tamil Epigraphy: A Deep Learning Approach for Vatteluttu Script Recognition — Manjunath G. Asuti, Venkatesh Bhandage, et al. · Journal of Internet Services and Information Security (2025) | TGRS Research Map | TGRS