The preservation of Balinese carving data is a challenge in recognition of Balinese carving. Balinese carvings are a cultural heritage found in traditional buildings in Bali. The collection of Balinese carving images from public images can be a solution for preserving cultural heritage. However, the lousy quality of taking photographs, e.g., skewed shots, can affect the recognition results. Research on the Balinese carving recognition has existed but only recognizes a predetermined image. We proposed a Neural Style Geometric Transformation (NSGT) as a data augmentation technique for Balinese carvings recognition. NSGT is combining Neural Style Transfers and Geometric Transformations for a small dataset solution. This method provides variations in color, lighting, rotation, rescale, zoom, and the size of the training dataset, to improve recognition performance. We use MobileNet as a feature extractor because it has a small number of parameters, which makes it suitable to be applied on mobile devices. Eight scenarios were tested based on image styles and geometric transformations to get the best results. Based on the results, the proposed method can improve accuracy by up to 16.2%.

Papyrus script is a cultural heritage in Bali. As we know, that the papyrus is a cultural matter which is rich in valuable cultural values. Issues or problems encountered today is that the papyrus are not well maintained. Thus, many papyrus becomes damaged because it is not stored properly. Papyrus script was written using Balinese script's characters which having different features compared with Latin's characters. Balinese script can be recognized with feature extraction owned by each Balinese script. KNN is a classification algorithm based on nearest neighborhood. KNN can be used to classify Balinese script's features so that the test Balinese script's features which having nearest neighborhood value with the trained Balinese script's features will be recognized as the same Balinese script.

Feature extraction is an important process in character recognition system. The purpose of this process is to obtain special feature from a character image. This paper is focuses on how to obtain special feature from a handwritten Balinese character image using zoning. This algorithm dividing Balinese character image into multiple regions, then a special feature on each region resulting the data extracted feature. The test result in this paper generates a various  semantic and direction feature data. This is because this paper using handwritten Balinese character. Furthermore, the features that produced in this paper can be used on Balinese character image recognition process

Balinese script is an important aspect that packs the Balinese culture from time to time which continues to experience development along with technological advances. Balinese script consists of three types (1) Wrésastra, (2) Swalalita and (3) Modre which have different types of characters. The Wrésastra and Swalalita script are Balinese scripts which grouped into the script criteria that are used to write in the field of everyday life. In this research, the zoning method will be implemented in the feature extraction process to produce special features owned by Balinese script. The results of the feature extraction process will produce special features owned by Balinese script which will be used in the classification process to recognize the character of Balinese script. Special features are produced using the zoning method, it will divide the image characters area of ??Balinese scripts into several regions, to enrich the features of each Balinese script. The result of feature extractions is stored as training data that will be used in the classification process. K-Nearest Neighbors is implemented in the special feature classification process that is owned by the character of Balinese script. Based on the results of the test, the highest level of accuracy was obtained using the value K=3 and reference=10 with the accuracy of Balinese script recognition 97.5%.

No abstract