Aksara jawa text detection in scene images using convolutional neural network

Abstract

Aksara jawa is an ancient Javanese character, which has been used since 17th century. The character is mostly written on stones to describe history or naming such as places, wedding, tombstones, etc. This character is however gradually ignored by people. Thus, it is extremely important to preserve this near loss heritage culture. In this paper, as a step toward preserving and converting visual information into text, we develop Aksara Jawa text detection system in scene images employing deep convolutional neural network to localize the occurrence of Aksara Jawa text. This method mainly differs from the existing Aksara Jawa text works that employ manually hand-crafted features and explicitly learn a classifier. The features and classifier of this method are jointly learned from which the back-propagation technique is employed to obtain parameters simultaneously. A text confidence map is then produced followed by bounding boxes formation which is estimated and formed to indicate the occurrence of text lines. Experiments show encouraging result for the benefit of text analysis on Aksara Jawa.