Abstract
Many similar shaped scripts are used all over the world today. Scripts identification with similar shaped characters is one of the difficulties in script identification field and it need to be resolved. However, there are a little report about identification of Central Asian countries and Chinese Minority scripts, which identification of similar scripts. In this paper, a multi-script database was established, which are including 2200 plain document images with different resolution in 11 scripts such as English, Chinese, Arabic, Russian, Uyghur, Mongol, Tibet, Turkish, Kyrgyzstani, Uzbekistani and Tajikistani. Then, HSV features were extracted from each whole page image and they were classified by using BP neural network classifier. After experiment in our system, it is achieved 88.14 % of average identification rate and 99.0 % of highest identification rate in our experiment with the dataset. Experimental results indicated that HSV features were effective feature for identify these scripts.
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References
Gopal, J., Saurabhand, D., Jayanthi, S.: A generalized framework for script identification. Int. J. Doc. Anal. Recogn. (IJDAR) 10(2), 55–68 (2007)
Dhanya, D., Ramakrishnan, A.G.: Script identification in printed bilingual documents. In: Lopresti, D., Hu, J., Kashi, R. (eds.) DAS 2002. LNCS, vol. 2423, pp. 13–24. Springer, Heidelberg (2002)
Pal, U., Sarkar, A.: Recognition of printed urdu script. In: Proceedings of the International Conference on Document Analysis and Recognition, Bangalore, pp. 1183–1187 (2003)
Spitz, A.L.: Script and language determination from document images. In: 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, USA, pp. 229–235 (1994)
Spitz, A.L.: Determination of the script and language content of document images. IEEE Trans. Pattern Anal. Mach. Intell. 19(3), 235–245 (1997)
Ul-Hasan, A., Afzal, M.Z., Shafait, F., Liwicki, M., Breuel, T.M.: A sequence learning approach for multiple script identification. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, pp. 1046–1050 (2015)
Pal, U., Chaudhuri, B.: Identification of different script lines from multi-script documents. Image Vis. Comput. 20(13–14), 945–954 (2002)
Shi, B., Bai, X., Cong, Y.: Script identification in the wild via discriminative convolutional neural network. Pattern Recogn. 52, 448–458 (2016)
Androutsos, D., Plataniotis, K.N., Venetsanopoulos, A.N.: A novel vector-based approach to color image retrieval using a vector angular-based distance measure. Comput. Vis. Image Underst. 75(1–2), 46–58 (1999). ISSN 1077-3142
Ferrer, M.A., Morales, A., Rodríguez, N., Pal, U.: Multiple training-one test methodology for handwritten word-script identification. In: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), Heraklion, pp. 754–759 (2014)
Pawan, K.S., Ram, S., Mita, N.: Offline script identification from multilingual indic-script documents: a state-of-the-art. Comput. Sci. Rev. 15–16, 1–28 (2015)
Obaidullah, S.M., Das, N., Halder, C., Roy, K.: Indic script identification from handwritten document images-An unconstrained block-level approach. In: 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), Kolkata, pp. 213–218 (2015)
Stricker, M., Orengo, M.: Similarity of color images. In: SPIE Storage and Retrieval for Image and Video Databases, vol. 2420, pp. 381–392(1995)
Roy, K., Pal, U., Chaudhuri, B.B.: Neural network based word-wise handwritten script identification system for Indian postal automation. In: Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, pp. 240–245(2005)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61363064, 61563052, 61163028), College Scientific Research Plan Project of Xinjiang Uyghur Autonomous Region (No. XJEDU2013I11), and Special Training Plan Project of Xinjiang Uyghur Autonomous Region’s Minority Science and Technological Talents (No. 201323121).
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Mijit, B., Aysa, A., Yadikar, N., Han, Xk., Ubul, K. (2016). Script Identification Based on HSV Features. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_48
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DOI: https://doi.org/10.1007/978-981-10-3005-5_48
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