Abstract
Palm leaf manuscripts are important source of history and ancient wisdom. Large number of manuscripts have been already digitized in the form of folio images. To extract useful information, an optical character recognition (OCR) is often considered to be the first step towards text mining. Unfortunately, folio images contain multiple unsegmented palm leaf images, making it difficult to manage in OCR process. This motivates us to propose a new page segmentation method for palm leaf manuscripts. This method consists of two main steps, first of which is the detection of objects in folio images using Connected Component Labeling method in a transformed L*a*b* color space. The second step is rule-based selection of objects as either palm leaf or not palm leaf. The experiments performed on 20 publicly available palm leaf manuscripts composed of 384 folio images demonstrated that the proposed method effectively segmented folio images into separate palm leaf images, with 99.86 % precision and 96.67 % recall scores.
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Acknowledgments
This study was funded under the Royal Golden Jubilee Ph.D. Program by the Thailand Research Fund. We would like to thank Faculty of Science and Lan na Studies, Chiang Mai University, Thailand, for financial support and collection of digital Lanna archives, respectively.
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Inkeaw, P., Bootkrajang, J., Charoenkwan, P., Marukatat, S., Ho, SY., Chaijaruwanich, J. (2016). Rule-Based Page Segmentation for Palm Leaf Manuscript on Color Image. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham. https://doi.org/10.1007/978-3-319-49304-6_16
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DOI: https://doi.org/10.1007/978-3-319-49304-6_16
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