| dc.description.abstract | Ancient Sinhalese and Tamil palm-leaf manuscripts are an important part of the Sri
Lankan cultural heritage, but their existence and accessibility are under a harsh siege
due to their material decay, incomprehensible handwriting, and inconsistent script.
The present paper is a systematic review of previously conducted research on AI based techniques of recognizing handwriting on historical manuscripts, particularly
in Sinhalese and Tamil scripts. The review reports on the previous studies regarding
datasets, preprocessing methods, deep learning architectures, evaluation metrics, and
reported performance. It has been found that Convolutional Neural Networks (CNNs),
Recurrent Neural Networks (RNNs), CNN-LSTM-based Handwritten Text Recognition
(HTR) models, Transformers, and Vision Transformers (ViT) are prevalent in the recent
research in the field of historical script recognition. Nevertheless, the fact that there
are no large, annotated collections of ancient Sinhala or Tamil manuscripts is the most
debilitating constraint. The review also points out some major obstacles associated with
work on manuscript degradation, script complexity, and scarcity of data, which explains
why these scripts are not well represented in the global research on text recognition
by hand. According to the gaps defined in this paper, the knowledge that has been
identified is summarized and a structured line of research is planned to facilitate the
further work of digitizing and preserving the Sri Lankan manuscript heritage with the
help of AI. | en_US |
