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Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System

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@article{IJASEIT9001,
   author = {Nwe Nwe Soe and Win Htay},
   title = {Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {6},
   year = {2019},
   pages = {2216--2222},
   keywords = {Myanmar palm leaf manuscripts; feature extraction; OCR; line segmentation; character segmentation.},
   abstract = {

This paper proposed Myanmar palm leaf manuscript handwriting OCR system. Each text area in the Myanmar palm-leaf manuscript is segmented. This segmented character text image is needed to be recognized to transform to Myanmar handwritten characters which express Myanmar’s precious historical and invaluable information. This paper involves two essential steps: preprocessing and feature extraction. The preprocessing is carried out to extract the attractive palm-leaf manuscript region from the Images automatically are taken by the camera and to support the enhanced images for subsequence processes of Myanmar character recognition from Myanmar palm leaves. The one-dimensional segmentation approach is used to crop leaf area in the image which is taken with high resolution. Line count analysis is also done to extract the region for using enough line count. After that, line segmentation is carried out using Object Frequency Histogram along the horizontal lines which can find the best optimal points between the lines. Similarly, the same technique but vertically is used to get each character or smallest group of characters. Totally 18 features are extracted to recognize the Myanmar palm-leaf manuscript characters. Although the experimental results are good enough but some difficulties are still needed to take account related to the connected components. 

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9001},    doi = {10.18517/ijaseit.9.6.9001} }

EndNote

%A Soe, Nwe Nwe
%A Htay, Win
%D 2019
%T Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System
%B 2019
%9 Myanmar palm leaf manuscripts; feature extraction; OCR; line segmentation; character segmentation.
%! Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System
%K Myanmar palm leaf manuscripts; feature extraction; OCR; line segmentation; character segmentation.
%X 

This paper proposed Myanmar palm leaf manuscript handwriting OCR system. Each text area in the Myanmar palm-leaf manuscript is segmented. This segmented character text image is needed to be recognized to transform to Myanmar handwritten characters which express Myanmar’s precious historical and invaluable information. This paper involves two essential steps: preprocessing and feature extraction. The preprocessing is carried out to extract the attractive palm-leaf manuscript region from the Images automatically are taken by the camera and to support the enhanced images for subsequence processes of Myanmar character recognition from Myanmar palm leaves. The one-dimensional segmentation approach is used to crop leaf area in the image which is taken with high resolution. Line count analysis is also done to extract the region for using enough line count. After that, line segmentation is carried out using Object Frequency Histogram along the horizontal lines which can find the best optimal points between the lines. Similarly, the same technique but vertically is used to get each character or smallest group of characters. Totally 18 features are extracted to recognize the Myanmar palm-leaf manuscript characters. Although the experimental results are good enough but some difficulties are still needed to take account related to the connected components. 

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9001 %R doi:10.18517/ijaseit.9.6.9001 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 6 %@ 2088-5334

IEEE

Nwe Nwe Soe and Win Htay,"Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, pp. 2216-2222, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.6.9001.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Soe, Nwe Nwe
AU  - Htay, Win
PY  - 2019
TI  - Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 6
Y2  - 2019
SP  - 2216
EP  - 2222
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Myanmar palm leaf manuscripts; feature extraction; OCR; line segmentation; character segmentation.
N2  - 

This paper proposed Myanmar palm leaf manuscript handwriting OCR system. Each text area in the Myanmar palm-leaf manuscript is segmented. This segmented character text image is needed to be recognized to transform to Myanmar handwritten characters which express Myanmar’s precious historical and invaluable information. This paper involves two essential steps: preprocessing and feature extraction. The preprocessing is carried out to extract the attractive palm-leaf manuscript region from the Images automatically are taken by the camera and to support the enhanced images for subsequence processes of Myanmar character recognition from Myanmar palm leaves. The one-dimensional segmentation approach is used to crop leaf area in the image which is taken with high resolution. Line count analysis is also done to extract the region for using enough line count. After that, line segmentation is carried out using Object Frequency Histogram along the horizontal lines which can find the best optimal points between the lines. Similarly, the same technique but vertically is used to get each character or smallest group of characters. Totally 18 features are extracted to recognize the Myanmar palm-leaf manuscript characters. Although the experimental results are good enough but some difficulties are still needed to take account related to the connected components. 

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9001 DO - 10.18517/ijaseit.9.6.9001

RefWorks

RT Journal Article
ID 9001
A1 Soe, Nwe Nwe
A1 Htay, Win
T1 Research and Development of Feature Extraction from Myanmar Palm Leaf Manuscripts for the Myanmar Character Recognition System
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 6
YR 2019
SP 2216
OP 2222
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Myanmar palm leaf manuscripts; feature extraction; OCR; line segmentation; character segmentation.
AB 

This paper proposed Myanmar palm leaf manuscript handwriting OCR system. Each text area in the Myanmar palm-leaf manuscript is segmented. This segmented character text image is needed to be recognized to transform to Myanmar handwritten characters which express Myanmar’s precious historical and invaluable information. This paper involves two essential steps: preprocessing and feature extraction. The preprocessing is carried out to extract the attractive palm-leaf manuscript region from the Images automatically are taken by the camera and to support the enhanced images for subsequence processes of Myanmar character recognition from Myanmar palm leaves. The one-dimensional segmentation approach is used to crop leaf area in the image which is taken with high resolution. Line count analysis is also done to extract the region for using enough line count. After that, line segmentation is carried out using Object Frequency Histogram along the horizontal lines which can find the best optimal points between the lines. Similarly, the same technique but vertically is used to get each character or smallest group of characters. Totally 18 features are extracted to recognize the Myanmar palm-leaf manuscript characters. Although the experimental results are good enough but some difficulties are still needed to take account related to the connected components. 

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9001 DO - 10.18517/ijaseit.9.6.9001