Development of optimized ensemble machine learning-based character segmentation framework for ancient Tamil palm leaf manuscripts

Abstract

Background

Character Segmentation plays a crucial role in analyzing the intricate script inscribed on these historical documents. Due to the unique script style and the physical characteristics of the palm leaves, character segmentation is considered to be challenging. Aim: An efficient character segmentation approach from palm-leaf manuscripts is developed in this work.

Method

ology: Initially, the images of Tamil palm leaf manuscripts are gathered manually and pre-processed with optimal binary thresholding and morphological operation. Then, the pre-processed images are utilized for Line segmentation by the Projection Profile method. The line-segmented images are fed into the feature extraction process. These extracted features are subjected to character segmentation by developing an Ensemble Machine Learning Structure (EMLS). EMLS is motivated by Bayesian Learning (BL), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Moreover, parameter optimization is performed using the Hybridization of Sandpiper with Fireworks Algorithm (HSFA). Therefore, the developed method is suitable for real-time applications like mobile document scanning and educational-based applications.

Result

The experimental analysis is made to declare the efficiency of the developed approach, and given the accuracy to be 95.53%.

Conclusion

The developed model provides the character-segmented outcome and offers a promising tool for palm-leaf character recognition.