Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (2): 115-121.doi: 10.6040/j.issn.1672-3961.0.2020.348

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Script identification of Central Asian document images based on LTP and HOG texture feature fusion

WU Zhengjian1, MUTALLIP Mamut2, HORNISA Mamat1, ALIM Aysa1,3, KURBAN Ubul1,3*   

  1. 1. School of Information Science Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China;
    2.The Library, Xinjiang University, Urumqi 830046, Xinjiang, China;
    3. The Key Lab. of Xinjiang Multilingual Information Technology, Urumqi 830046, Xinjiang, China
  • Published:2021-04-16

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Abstract: Due to the existence of a number of scripts with high similarity in Central Asia, a document image script identification method based on the cross-fusion of a unified local ternary pattern(riu2-LTP)with rotational invariance and histogram of oriented gradients(HOG)features was proposed. An SVM classifier was used to perform experiments on a database containing a total of 10 000 images of 10 scripts. In order to improve multi-script identification, Bayesian optimized SVM hyperparameters were used. The method first extracted riu2-LTP with a radius of and a sampling 8 points for the document images; HOG was extracted from the database again; the cross-fusion method was to incorporate the 20-dimensional riu2-LTP features and 36-dimensional HOG features sequentially into the new feature set, respectively. The experiments showed that the average recognition rate of this method reached 99%, which was better than the single LTP, riu2-LTP, and HOG methods.

Key words: LTP, HOG, feature fusion, Bayesian optimization, script identification

CLC Number: 

  • TP391
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