Palm Leaf Manuscript Segmentation and Reading by Using Artificial Neural Networks
Journal Title: International Journal of the Computer, the Internet and Management - Year 2018, Vol 26, Issue 2
Abstract
Palm leaf manuscript is considered as a kind of cultural heritage and the record of local wisdom of ancestors that should be transformed into digital format for educational and research benefits of the next generation. This research presents palm leaf manuscript’s Isan Dhamma characters segmentation and reading conducted by using image processing. The objective of this research is to utilize the obtained data in sentence recognition process further. The input was digital photos of a palm leaf manuscript written with Isan Dhamma characters that was proposed to be adjusted on its quality by adjusting light intensity through histogram. Subsequently, its quality was improved by using median filter in order to screen data on enhancement or attenuation of some picture’s properties in order to gain quality as demanded. Subsequently, characters were segmented from background (segmentation) by using Global Thresholding. In the next process, Principal Components Analysis (PCA) was used in order to match each sentence with its features. In the last procedure, obtained features were compared with database in order to find range of such sentence by using the principles of neural network before comparing with support vector machine in order to compare such sentence with database. Based on the experiment conducted with 10 images of palm leaf manuscript, it was found that neural network gave better results than support vector machine by 86%.
Authors and Affiliations
Thanannath Phattharachairawee, Montean Rattanasiriwongwut, Mahasak Ketcham
Keywords
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