TY - GEN
T1 - Automatic counting based on scanned election form using feature match and convolutional neural network
AU - Waladi, Akhiyar
AU - Arymurthy, Aniati Murni
AU - Wibisono, Ari
AU - Mursanto, Petrus
PY - 2019/10
Y1 - 2019/10
N2 - In a democratic state, General Election (Pemilu) is a procedure for selecting regional heads regulated in Article 1 paragraph 3 of the 1945 Constitution. KPU (Komisi Pemilihan Umum) is a state institution that organizes by prioritize transparency and accountability in each stage of general elections in Indonesia. One form of openness that has always been in the media spotlight is the vote counting process. The manual calculation process carried out by the General Election Commissions (KPU) on form C1 is time-consuming and resourceful because it involves paid volunteers. In this study, the authors used the proposed method to build a numerical handwriting recognition system on the C1 KPU form. Method proposed is a recognition flow including table detection with candidate contour techniques, feature matching, number segmentation, and digit classification with the convolutional neural network (CNN). The datasets used are from the official KPU election websites in 2014 and 2019. We use capsnet to classify each segmented digit with 95.65% accuracy. The trained model was tested using validation form and reach 80.73% document accuracy using 2019 election form.
AB - In a democratic state, General Election (Pemilu) is a procedure for selecting regional heads regulated in Article 1 paragraph 3 of the 1945 Constitution. KPU (Komisi Pemilihan Umum) is a state institution that organizes by prioritize transparency and accountability in each stage of general elections in Indonesia. One form of openness that has always been in the media spotlight is the vote counting process. The manual calculation process carried out by the General Election Commissions (KPU) on form C1 is time-consuming and resourceful because it involves paid volunteers. In this study, the authors used the proposed method to build a numerical handwriting recognition system on the C1 KPU form. Method proposed is a recognition flow including table detection with candidate contour techniques, feature matching, number segmentation, and digit classification with the convolutional neural network (CNN). The datasets used are from the official KPU election websites in 2014 and 2019. We use capsnet to classify each segmented digit with 95.65% accuracy. The trained model was tested using validation form and reach 80.73% document accuracy using 2019 election form.
KW - Convolutional Neural Network
KW - Election
KW - Feature Matching
KW - Optical Character Recognition
UR - http://www.scopus.com/inward/record.url?scp=85081082019&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS47736.2019.8979691
DO - 10.1109/ICACSIS47736.2019.8979691
M3 - Conference contribution
T3 - 2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
SP - 193
EP - 198
BT - 2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
Y2 - 12 October 2019 through 13 October 2019
ER -