Pengenalan Tulisan Tangan Huruf Latin Bersambung Menggunakan Local Binary Pattern dan K-Nearest Neighbor

Abstract

There are 26 Latin letters in Indonesia, 5 of which are vowels and 21 consonants. This study will translate handwriting with a Latin object using the K-Nearest Neighbor method with the Local Binary Pattern extension. The research is being done with a focus on experimentation using a few methods that have already been discussed. Concatenated Latin letters have a few variations that depend on the work's author, so research will be conducted to identify cursive Latin letters based on these variations. Each of the 30 respondents wrote 26 capital letters and 26 lowercase letters on paper, which was then scanned to provide the image data. Black, blue, and red pens were used to write by every ten responders. The recognition procedure is broken into two halves, capital and non-capital letter recognition using 780 picture datasets each. In the study, k-fold cross-validation is used, with k = 6. The best value was reached at k = 7 with 29.49 percent accuracy, 33.88 percent precision, recall 33.46 percent, and F1-score 27.65 percent according to the research utilizing KNN with values k = 3, 5, and 7. and for recognizing non-capital characters, the best result was found at k=3 with accuracy, precision, recall, and F1-score of 26.28, 27.27, and 22.7%, respectively.