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Tamil handwritten palm leaf manuscript dataset (THPLMD).

I Jailingeswari¹, S Gopinathan¹

¹Department of Computer Science, University of Madras, Chennai, India.

|February 15, 2024

Summary

This study presents a new dataset of 262 deteriorated Tamil palm leaf manuscripts, crucial for advancing machine learning and artificial intelligence research. The dataset aids in preserving and analyzing these historically significant texts.

Keywords:

Binarization Enhancement Ground truth Otsu Photoshop Segmentation Tamil palm leaf

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Area of Science:

Digital Humanities
Computer Vision
Manuscriptology

Background:

Palm leaf manuscripts are vital historical records but often exist in deteriorated states.
Challenges in research include physical degradation like cracks, discoloration, and insect damage.
Digitization and analysis of these manuscripts are essential for preservation and study.

Purpose of the Study:

To create a high-quality, accessible dataset of deteriorated Tamil palm leaf manuscripts.
To facilitate research in machine learning, artificial intelligence, and related fields.
To aid in the preservation and study of historical Tamil texts.

Main Methods:

Collected 262 deteriorated Tamil palm leaf manuscript samples ('Naladiyar', 'Tholkappiyam', 'Thirikadugam').
Captured high-quality images using a Nikon camera.
Pre-enhanced images using editing software and applied Otsu thresholding for binarization.

Main Results:

Developed a readily accessible dataset of binarized manuscript images.
The dataset comprises samples focusing on mortal health, discipline, and Tamil grammar.
The process, though time-consuming, yields valuable ground images for AI applications.

Conclusions:

The created dataset is a significant contribution to the field of digital humanities and AI.
This resource will accelerate research in machine learning, deep learning, and artificial neural networks for manuscript analysis.
Enables further computational study and preservation of endangered Tamil literary heritage.