CHARACTER IMAGE SEGMENTATION OF JAVANESE SCRIPT USING CONNECTED COMPONENT METHOD

Automation of Javanese script translation is needed to make it easier for people to understand the meaning of ancient Javanese script. By using Javanese script image as input, the translation system generally consists of character segmentation, character recognition, and combining the recognized characters as a meaningful word. The segmentation which obtains region of interest of each character, is an important process in the translation system. In the previous research, segmentation using projection profile method can separate each character well. The method can overcome characters overlapping, but it still produces truncated characters. In this study, we proposed a new segmentation to reduce the truncated character. The first step of the proposed method is pre-processing that consists of converting input into binary image and cleaning noises. The next step is to determine the connected component labels, which further perform as candidate of characters. Some of the candidates are still represented by more than one labels, so that we need a process to merge the connected component labels that have centroid distance less than threshold. We evaluate the proposed method using Intersection over Union (IoU). The evaluation shows the best accuracy 93,26%

The Improvement of Watershed Algorithm Accuracy for Image Segmentation Handwritten Numbered Musical Notation

In the Implementation of image processing to translate the image of the numbered musical notation into a numerical character requires some initial process that must be passed like image segmentation process. The advantage of successful segmentation process is that it can reduce the failure rate in the object recognition process. Segmentation process determines the success of object recognition process, it takes segmentation algorithm that can perform accurate object separation. The combination segmentation process developed in this research used projection profile algorithm, watershed and non object filtering. Profile projection algorithm is used to crop the image of the musical horizontally and vertically. The watershed algorithm is used to segment the numerical object of numerical notation generated from the projection profile process. Non object filtering is a continuation of the watershed algorithm that includes the non-object reduction process and the process of combining objects so that the original object segment will be generated. The based on the results of the research, the accuracy of the segment on watershed segmentation is 99.74% higher than watershed segmentation without combination of 94.82%

Segmentasi Aksara Pada Tulisan Aksara Jawa Menggunakan Adaptive Threshold

Penelitian mengenai Aksara Jawa sudah banyak digunakan. Salah satunya adalah penelitian mengenai naskah pada Aksara Jawa. Kondisi naskah Aksara Jawa sebagian besar dalam kondisi baik meskipun masih terdapat beberapa halaman yang robek dan warna kertas yang memudar. Hal ini disebabkan karena umur kertas yang sudah puluhan tahun lebih dan bahan kertas yang kurang baik. Penelitian ini difokuskan hanya untuk membagi aksara pada citra tulisan tangan menjadi karakter-karakter aksara yang dapat digunakan dalam pengenalan Aksara Jawa pada penelitian selanjutnya. Penelitian ini terdapat lima proses, yaitu akuisi citra, proses preprocessing, proses segmentasi, dilasi, dan pelabelan Aksara. Pada proses segmentasi, penelitian ini menggunakan adaptive threshold. Metode adaptive threshold dapat digunakan pada segmentasi citra Aksara Jawa karena metode ini memilih nilai threshold berdasarkan variasi intensitas tiap lokal window. Hasil nilai akurasi yang didapat dari penelitian ini yaitu sebesar 88.60% dari 30 data citra Aksara Jawa. ====================================================================================== Research on character java there have been many used. One of them is research on manuscript in character java. The condition manuscript character java mostly in good condition although there is still several pages torn and color paper faded. This is because age paper already dozens of more years and materials paper a less well. Research is focused only to divide character in image handwriting be the characters characters that can be used in the introduction of character java in the next research. This research there are five the process, image aquatition, the preprocessing, the process segmentation, dilations, and the labeling character. To the process segmentation, this research using adaptive threshold. A method of adaptive threshold can be used on segmentation image character java because this method choose threshold value based on variations in intensity every local window. The results of value accuracy obtained from the study is as much as 88.60% of 30 image data character java