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Aksara jawa text detection in scene images using convolutional neural network

机译：Aksara Jawa使用卷积神经网络的场景图像中的文本检测

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摘要

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.
机译：Aksara Jawa是一个古老的爪哇角色，自17世纪以来已经使用过。该角色主要是写在石头上，以描述历史或命名，如地方，婚礼，墓碑等。然而，这种角色逐渐被人忽视。因此，保持这种近损失遗产文化是非常重要的。在本文中，作为将视觉信息保持和转换为文本的步骤，我们在采用深卷积神经网络的场景图像中开发Aksara Jawa文本检测系统，以定位Aksara Jawa文本的发生。这种方法主要与现有的Aksara Jawa文本的作品不同，使用手动手工制作的功能并明确地学习分类器。该方法的特征和分类是共同学习的，从中采用后传播技术同时获得参数。然后产生文本置信度图，然后产生据估计和形成的边界框，以指示文本线的发生。实验表明，令人鼓舞的结果是对Aksara Jawa的文本分析的益处。

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