Abstract
With the continuous development of filtration technology for text information, many criminal offenders made much harmful text information in Uyghur involving extreme religion and terrorism information by image editing software. In order to recognize the Uyghur text-images effectively, a scheme for recognizing printed Uyghur based on the features extracted by histogram of oriented gradient (HOG) and the multilayer perceptron (MLP) neural network is put forward. Firstly, preprocess the Uyghur text-images to obtain the binary images after eliminating noise. After that, segment the text-line by horizontal projection integral method and segment the words and characters by vertical projection integral method to obtain independent characters. Next, extract the features of characters by HOG. Finally, recognize the characters through the trained MLP neural network classifier and according to features extract by HOG. The experimental results showed that we could recognize Uyghur characters accurately by the method put forward.
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Acknowledgments
This paper is supported by the National Natural Science Foundation of China (NSFC) (No. 61762086), the National Social Science Fund of China (No. 13CFX055) and the Science Research Program of the Higher Education Institute of Xinjiang (No. XJEDU2016I052, XJEDU2016S090, XJEDU2017M046).
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Aizezi, Y., Jiamali, A., Abdurixiti, R., Ubul, K. (2018). Research on the Methods for Extracting the Sensitive Uyghur Text-Images for Digital Forensics. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_75
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DOI: https://doi.org/10.1007/978-3-319-97909-0_75
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