使用深度学习识别图像中的字体和泰米尔字母

Q3 Economics, Econometrics and Finance
Manikandan Sridharan, Delphin Carolina RANI ARULANANDAM, R. K. Chinnasamy, Suma Thimmanna, Sivabalaselvamani Dhandapani
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引用次数: 6

摘要

本文提出了一种深度学习方法从包含文本的图像中识别泰米尔字母。这是识别过程,将图像中的文字划分为字母或字符。每个被识别的字母被发送到识别系统,并使用深度学习算法过滤文本。该算法采用卷积神经网络方法实现了字母与文本的分离。过滤系统用于根据找到的字母来识别字体。泰米尔字母是测试数据,并加载到识别系统中。训练后的数据输入包含从图像中过滤的字母。例如,泰米尔字母如在测试数据集中可用。将训练后的数据应用于深度卷积神经网络处理。创建了两个数据集,其中包含带有泰米尔字母的测试数据,第二个数据集用于识别输入数据或训练数据。取1.5万个字母,用字体和字母创建512 X 512 X 3大小的深度卷积网络。结果,85%的泰米尔字母被识别,82%的泰米尔字母使用字体进行测试。TensorFlow用于测试准确率和成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING
This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.
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来源期刊
Applied Computer Science
Applied Computer Science Engineering-Industrial and Manufacturing Engineering
CiteScore
1.50
自引率
0.00%
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0
审稿时长
8 weeks
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