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Thai Text Detection and Classification Using Convolutional Neural Network

机译:卷积神经网络的泰语文本检测与分类

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Many foreign people don’t know Thai language and most of the time Thai sign images, posters or text images do not have subtitles in English so, it is very necessary to have a system that can translate Thai text to English. In this paper, MSER and convolutional neural network (CNN) have used to understand Thai text in English. Firstly, region of interest has localized from natural image which is some particular Thai text. Then text has extracted and fed to CNN. We used a 7-layer self-designed CNN model that provides the output with an accuracy of 98%. The proposed system takes natural scene image as input and uses MSER, geometrical properties as well as bounding box algorithm to localize the text area then selected localized areas have fed to CNN and provide an output that has the English meaning for the Thai text image. This paper introduces a new approach of text translation by using image classification method. The proposed system can work on particular inputs which are indoor sign Thai text images.
机译:许多外国人不懂泰语,而且大多数情况下,泰国的招牌图像,海报或文字图像都没有英文字幕,因此,必须有一个能够将泰文翻译成英文的系统。本文使用MSER和卷积神经网络(CNN)来理解英语中的泰语文本。首先,感兴趣的区域已经从自然图像中定位,该自然图像是一些特定的泰国文字。然后,文本已提取并送入CNN。我们使用了7层自行设计的CNN模型,该模型提供了98%的精度输出。拟议的系统以自然场景图像为输入,并使用MSER,几何属性以及边界框算法对文本区域进行本地化,然后将选定的局部区域馈送到CNN并提供具有泰语文本图像英文含义的输出。介绍了一种利用图像分类法进行文本翻译的新方法。所提出的系统可以在特定的输入上工作,这些输入是室内标语泰语文字图像。

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