Development of an effective character segmentation and efficient feature extraction technique for malayalam character recognition from palm leaf manuscripts

Sudarsan, Dhanya; Sankar, Deepa

doi:10.1007/s12046-023-02181-5

Sādhanā

2023

DOI: 10.1007/s12046-023-02181-5

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Development of an effective character segmentation and efficient feature extraction technique for malayalam character recognition from palm leaf manuscripts

Dhanya Sudarsan,

Deepa Sankar

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2024

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Cited by 4 publications

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Enhancing Malayalam Palm Leaf Character Segmentation: An Improved Simplified Approach

Sudarsan,

Sankar

2024

SN COMPUT. SCI.

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Enhancing Malayalam Palm Leaf Character Segmentation: An Improved Simplified Approach

Sudarsan,

Sankar

2024

SN COMPUT. SCI.

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An Ensemble Neural Network Model For Malayalam Character Recognition From Palm Leaf Manuscripts

Sudarsan,

Sankar

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Palm leaf manuscripts (PLMs), crucial for ancient communication hold a wealth of information encompassing culture, art, literature, religion, and medicinal wisdom. Malayalam, Kerala's official language, significantly contributes to medical sciences, making palm scripts invaluable, especially in times of pandemics. This study introduces a ground-breaking model for automatic recognition of characters in Malayalam palm scripts. This is the first significant deep learning-based attempt, to our knowledge, to automate Malayalam character recognition in PLMs. The developed model is a fusion of fine-tuned Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM). Discriminative features were extracted from each character in the manuscript through multiple convolutional layers, and these feature vectors were then classified into their respective character classes using an ensemble deep learning model. The performance of the proposed method was evaluated using a self-generated dataset of old Malayalam PLMs from the period 1800 to 1908 AD. Overcoming challenges such as complex morphology, large character set, similar characters, and a unique writing style, the model achieved an impressive accuracy of 96.40%, outperforming state-of-the-art systems. Notably, the model obtained a negative predictive value (NPV) of 99.3%, positive predictive value (PPV) of 83.33%, sensitivity of 79.55%, specificity of 99.45% and F-Measure of 88.39%.Thus this advancement marks a significant milestone in automatic transcriptions providing a crucial tool for doctors and researchers.

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Automatic damage identification of Sanskrit palm leaf manuscripts with SegFormer

Wang,

Wen,

Zhou

et al. 2024

Herit Sci

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Palm leaf manuscripts (PLMs) are of great importance in recording Buddhist Scriptures, medicine, history, philosophy, etc. Some damages occur during the use, spread, and preservation procedure. The comprehensive investigation of Sanskrit PLMs is a prerequisite for further conservation and restoration. However, current damage identification and investigation are carried out manually. They require strong professional skills and are extraordinarily time-consuming. In this study, PLM-SegFormer is developed to provide an automated damage segmentation for Sanskrit PLMs based on the SegFormer architecture. Firstly, a digital image dataset of Sanskrit PLMs (the PLM dataset) was obtained from the Potala Palace in Tibet. Then, the hyperparameters for pre-processing, model training, prediction, and post-processing phases were fully optimized to make the SegFormer model more suitable for the PLM damage segmentation task. The optimized segmentation model reaches 70.1% mHit and 51.2% mIoU. The proposed framework automates the damage segmentation of 10,064 folios of PLMs within 12 h. The PLM-SegFormer framework will facilitate the preservation state survey and record of the Palm-leaf manuscript and be of great value to the subsequent preservation and restoration. The source code is available at https://github.com/Ryan21wy/PLM_SegFormer.

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Development of an effective character segmentation and efficient feature extraction technique for malayalam character recognition from palm leaf manuscripts

Cited by 4 publications

References 47 publications

Enhancing Malayalam Palm Leaf Character Segmentation: An Improved Simplified Approach

Enhancing Malayalam Palm Leaf Character Segmentation: An Improved Simplified Approach

An Ensemble Neural Network Model For Malayalam Character Recognition From Palm Leaf Manuscripts

Automatic damage identification of Sanskrit palm leaf manuscripts with SegFormer

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