Digit Classification of Majapahit Relic Inscription using GLCM-SVM

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %

Klasifikasi Topeng Pandawa dengan SVM

Klasifikasi merupakan tahapan tingkat lanjut dari sebuah keilmuan computer vision. Karena tujuan dari sebuah aplikasi rekognisi yaitu mengenali. Cara mengenali yaitu dengan cara klasifikasi. Banyak metode klasifikasi yang ada, namun pada penelitian ini menggunakan Support Vector Machine (SVM). SVM dipilih karena bisa mengatasi data dengan dimensi yang sangat besar tanpa mereduksi data, bekerja dengan data linier atau nonlinier dan membuat sebuah hyperplane yang memisahkan data antar kelas. Pada penelitian ini menggunakan data patung pandawa dengan lima kelas. Lima kelas terdiri dari kelas yudhistira, bima, arjuna, nakula dan sadewa. Kernel yang digunakan pada penelitian ini menggunakan  Radial Basis Function (RBF). Hasil ujicoba pada penelitian mempunya rata-rata akurasi sebesar 0,848

Arabic handwriting recognition using sequential minimal optimization

Due to the variability of writing styles and to other problems related to the nature of Arabic scripts, the recognition of Arabic handwriting is still awaiting accurate results. Segmentation of Arabic handwritten words into graphemes poses a major challenge in Arabic handwriting recognition and is highly error prone. In this paper, we adopt the holistic approach which handles the whole word image without any segmentation step. A set of different statistical features were investigated in this paper, namely, the Invariant Moments (IV), Histogram of Oriented Gradients (HOG) and the Gabor features. The classifier used is the Sequential Minimal Optimization (SMO) algorithm which is an improvement of the Support Vector Machines (SVM). The dataset used is AHDB which consists of 3045 images containing the most commonly used Arabic words written by one hundred different writers. The application of the features used with the SMO algorithm resulted in 91.5928 % correct classification.This publication was made possible by NPRP grant # NPRP NPRP7-442-1-082 from Qatar National Research fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu