TY - GEN
T1 - Brittle Ancient Document Using Adaptive Local Thresholding
AU - Muhtadin,
AU - Fatimah, Kiki
AU - Suprapto, Yoyon K.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The ancient manuscripts must be preserved wholeness and authenticity because there is much important information. The manuscripts are usually kept in libraries and museums for long periods of time. Because of this, the manuscript suffered damages due to a paper that is old. Therefore, prevention needs to be done. One of them by turning the ancient script into a digital image obtained by capturing the image with the camera. However, when an ancient manuscript has become a digital image, the noise on the paper will also be visible. Therefore, image segmentation is needed. Segmentation is the process of separating an object from the background. This research uses five test data and the method used is Mean-C Method, Sauvola Method, and Niblack Method. The evaluation using MSE and PSNR based on ground-truth. Of the three methods, the visual evaluation of the Sauvola Method and the Mean-C Method are good results and the Niblack Method still left many noises. The average measurements of MSE and PSNR from the five data from the Mean-c Method are 2813,71 and 75,58 dB, the results of MSE and PSNR Sauvola Methods are 3308.16 and 70,152 and the last test of the Niblack Method of MSE and PSNR are 8998, 51 and 45.99 dB.
AB - The ancient manuscripts must be preserved wholeness and authenticity because there is much important information. The manuscripts are usually kept in libraries and museums for long periods of time. Because of this, the manuscript suffered damages due to a paper that is old. Therefore, prevention needs to be done. One of them by turning the ancient script into a digital image obtained by capturing the image with the camera. However, when an ancient manuscript has become a digital image, the noise on the paper will also be visible. Therefore, image segmentation is needed. Segmentation is the process of separating an object from the background. This research uses five test data and the method used is Mean-C Method, Sauvola Method, and Niblack Method. The evaluation using MSE and PSNR based on ground-truth. Of the three methods, the visual evaluation of the Sauvola Method and the Mean-C Method are good results and the Niblack Method still left many noises. The average measurements of MSE and PSNR from the five data from the Mean-c Method are 2813,71 and 75,58 dB, the results of MSE and PSNR Sauvola Methods are 3308.16 and 70,152 and the last test of the Niblack Method of MSE and PSNR are 8998, 51 and 45.99 dB.
KW - Adaptive Local Thresholding
KW - Ancient Document Segmentation
KW - Brittle Ancient Document
KW - Image Processing
UR - http://www.scopus.com/inward/record.url?scp=85066505668&partnerID=8YFLogxK
U2 - 10.1109/CENIM.2018.8711191
DO - 10.1109/CENIM.2018.8711191
M3 - Conference contribution
AN - SCOPUS:85066505668
T3 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
SP - 214
EP - 220
BT - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Y2 - 26 November 2018 through 27 November 2018
ER -