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首页> 外文会议>2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing >Aksara jawa text detection in scene images using convolutional neural network
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Aksara jawa text detection in scene images using convolutional neural network

机译：利用卷积神经网络对场景图像中的Aksara颌骨文本进行检测

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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颌骨是一种古老的爪哇语字符，自17世纪以来一直使用。该角色大多写在石头上以描述历史或命名，例如地点，婚礼，墓碑等。但是，此角色逐渐被人们所忽略。因此，保留这种濒临灭绝的遗产文化非常重要。在本文中，为了保持视觉信息并将其转换为文本，我们使用深度卷积神经网络开发了Aksara Jawa文本检测系统，用于场景图像中，以定位Aksara Jawa文本的出现。此方法与现有的Aksara Jawa文本作品主要不同，后者使用手工手工制作的功能并明确学习分类器。共同学习了该方法的特征和分类器，从中可以采用反向传播技术同时获取参数。然后产生文本置信度图，然后形成边界框，估计并形成边界框以指示文本行的出现。实验表明，对Aksara Jawa进行文本分析的好处令人鼓舞。

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