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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Chinese Characters Recognition from Screen-Rendered Images Using Inception Deep Learning Architecture

verfasst von : Xin Xu, Jun Zhou, Hong Zhang, Xiaowei Fu

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

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Abstract

Text recognition from images can substantially facilitate a wide range of applications. However, screen-rendered images pose great challenges to current methods due to its low resolution and low signal to noise ratio properties. This paper proposed a Chinese characters recognition model using inception module based convolutional neural networks. Chinese characters were firstly extracted using vertical projection and error correction; then it can be recognized via inception module based convolutional neural networks. The proposed model can effectively segment Chinese characters from screen-rendered images, and significantly reduce the training time. Extensive experiments have been conducted on a number of screen-rendered images to evaluate the performance of the proposed model against state-of-the-art models.

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Vorheriges Kapitel Multi-scale Discriminative Patches for Fined-Grained Visual Categorization
Nächstes Kapitel Visual Tracking by Deep Discriminative Map
Literatur
1.
Masood, S.Z., Shu, G., Dehghan, A., Ortiz, E.G.: License plate detection and recognition using deeply learned convolutional neural networks. arXiv preprint arXiv:​1703.​07330 (2017)
2.
Yang, F., Jin, L., Yang, W., Feng, Z., Zhang, S.: Handwritten/printed receipt classification using attention-based convolutional neural network. In: 15th International Conference on Frontiers in Handwriting Recognition, pp. 384–389 (2016)
3.
Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649 (2012)
4.
Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: 12th International Conference on Document Analysis and Recognition, pp. 1464–1470 (2013)
5.
Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In: 13th International Conference on Document Analysis and Recognition, pp. 846–850 (2015)
6.
Zhang, X.-Y., Bengio, Y., Liu, C.-L.: Online and offline handwritten chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit. 61, 348–360 (2016)CrossRef
7.
Xiao, X., Jin, L., Yang, Y., Yang, W., Sun, J., Chang, T.: Building fast and compact convolutional neural networks for offline handwritten chinese character recognition. arXiv preprint arXiv:​1702.​07975 (2017)CrossRef
8.
Wachenfeld, S., Klein, H.U., Jiang, X.: Recognition of screen-rendered text. In: 18th International Conference on Pattern Recognition, pp. 1086–1089 (2006)
9.
Rashid, S.F., Shafait, F., Breuel, T.M.: An evaluation of HMM-based techniques for the recognition of screen rendered text. In: 11th International Conference on Document Analysis and Recognition, pp. 1260–1264 (2011)
10.
Wachenfeld, S., Klein, H.U., Jiang, X.: Annotated databases for the recognition of screen-rendered text. In: 9th International Conference on Document Analysis and Recognition, pp. 272–276 (2007)
11.
Bissacco, A., Cummins, M., Netzer, Y., Neven, H.: Photoocr: reading text in uncontrolled conditions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 785–792 (2013)
12.
Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. Int. J. Comput. Vision 116(1), 1–20 (2016)MathSciNetCrossRef
13.
Xu, X., Mu, N., Zhang, X., Li, B.: Covariance descriptor based convolution neural network for saliency computation in low contrast images. In: International Joint Conference on Neural Networks, pp. 616–623 (2016)
14.
Xu, X., Mu, N., Zhang, H., Fu, X.: Salient object detection from distinctive features in low contrast images. In: IEEE International Conference on Image Processing, pp. 3126–3130 (2015)
15.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
16.
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1106–1114 (2012)
17.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2015)
18.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
19.
20.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:​1512.​00567 (2015)
21.
Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:​1406.​2227 (2014)
22.
Ren, X., Chen, K., Sun, J.: A CNN based scene Chinese text recognition algorithm with synthetic data engine. arXiv preprint arXiv:​1604.​01891 (2016)
Metadaten
Titel
Chinese Characters Recognition from Screen-Rendered Images Using Inception Deep Learning Architecture
verfasst von
Xin Xu
Jun Zhou
Hong Zhang
Xiaowei Fu
Copyright-Jahr
2018
Buch
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77380-3_69

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