2019
DOI: 10.1109/access.2019.2911964
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Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images

Abstract: Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multilanguage environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used a… Show more

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Cited by 66 publications
(18 citation statements)
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“…Therefore, this paper proposes the structure of BR-CNN for fault identification, as shown in Fig. 1, where C layer represents convolution layer, P layer represents pooling layer, and F layer represents fully connected layer [21]. It can be seen that BR-CNN can set multiple input branches, so different fault characteristic parameters can be used.…”
Section: A Structure Of Br-cnnmentioning
confidence: 99%
See 4 more Smart Citations
“…Therefore, this paper proposes the structure of BR-CNN for fault identification, as shown in Fig. 1, where C layer represents convolution layer, P layer represents pooling layer, and F layer represents fully connected layer [21]. It can be seen that BR-CNN can set multiple input branches, so different fault characteristic parameters can be used.…”
Section: A Structure Of Br-cnnmentioning
confidence: 99%
“…Each weight matrix is obtained by training and learning from a convolution kernel. CNN extracts features of input data through multiple convolution kernels, so as to realize deep mining of features [21]. Taking the αth convolution kernel as an example, the calculation process from input layer to convolution layer can be expressed as…”
Section: A Structure Of Br-cnnmentioning
confidence: 99%
See 3 more Smart Citations