Somantri, Pascal Aditia Muclis, Ivan Kharisma
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引用次数: 1

摘要

社区的每个区域都有信息、新闻、公告或通知,通过印刷媒体,如图片、横幅、海报和纸张。然而,有许多外语(英语)的信息使得人们不理解的语言,因为人们只理解自己的区域语言(Sundanese)和缺乏媒体来帮助人们理解区域语言(Sundanese)到外语(英语)。因此,作者对基于android的文本检测进行了研究。文本检测或称为光学字符识别(OCR)是一种系统,它可以检测到可以以这种方式处理的数据文件中的文本。本研究中收集数据或信息的方法采用定性方法,开发本系统的方法采用瀑布法,该方法具有能够加速系统开发的优点,并且使用反向传播算法进行文本识别。反向传播算法是一种能够将误差最小化,从而使文本检测更加准确的算法。文本检测正确率为97%,错误正确率为3%,文本正确率结果取自14个样本,68个单词。文本检测的结果受到几种情况的影响,例如文本字体的类型、图像中的光线水平以及拍摄照片或图像的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Penerapan Algoritma Backpropagation Untuk Text Recognition Yang Ditranslate Ke Bahasa Daerah
Every area of the community there is information, news, announcements or notifications through print media such as pictures, banners, posters and paper. However, there are many foreign languages (English) in the information which makes people not understand the language, because people only understand their own regional language (Sundanese) and the lack of media to help people understand the regional language (Sundanese) to a foreign language (English). Therefore, the author takes research on android-based text detection. Text detection or called Optical Character Recognition (OCR) is a system that can detect text into data files that can be processed in such a way. The method of collecting data or information in this study uses qualitative, the method for developing this system uses the waterfall method which has the advantage of being able to accelerate system development and using a backpropagation algorithm for text recognition. Backpropagation algorithm is an algorithm that can minimize errors, so as to make text detection more accurate. The results of the text detection accuracy are 97% and the error accuracy results are 3%, the text accuracy results are taken from a total of 14 samples, and 68 words. The results of text detection are affected by several cases such as the type of text font, the level of light in the image and the direction in which the photo or image is taken.
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