LONTAR_DETC: Dense and High Variance Balinese Character Detection Method in Lontar Manuscripts

This paper proposed LONTAR_DETC, a method to detect handwritten Balinese characters in
Lontar
manuscripts. LONTAR_DETC is a deep learning architecture based on YOLO. The detection of Balinese characters in
Lontar
manuscripts is challenging due to the characteristics of Balinese characters in Lontar manuscripts. Balinese characters in
Lontar
manuscripts are dense, overlapping, have high variance, contain noise, and classes of these characters are imbalanced. The proposed method consists of three steps, namely data generation,
Lontar
manuscript annotation, and Balinese character detection. The first step is data generation, in which synthetic images of original
Lontar
manuscript images are generated with enhanced image quality. The second step is data annotation to build a new
Lontar
manuscript dataset. As a result, we also propose the Handwritten Balinese Character of
Lontar
manuscript (HBCL_DETC) dataset, a novel Balinese character in
Lontar
manuscripts dataset. HBCL_DETC contains 600 images that consists of more than 100,000 Balinese characters annotated by experts. Finally, the third step is training the YOLOv4 detection model using the HBCL_DETC dataset. We created this dataset specifically for the task of detecting Balinese characters in
Lontar
manuscripts. To evaluate the reliability of the dataset, we experimented with three scenarios. In the first scenario, the detection model was trained using original images of
Lontar
manuscripts, in the second scenario the detection model was trained with the addition of augmented grayscale images, and in the third scenario the detection model was trained using HBCL_DETC. Based on the experimental results, LONTAR_DETC can detect Balinese characters with high detection rate with mAP of 99.55%.