Abstract
This paper proposes a recognition technique which applies a combination of image processing and pattern recognition to visual features of individual words. Uyghur script is naturally cursive, and its characters have uneven width. Therefore, in image format, precisely cutting Uyghur words into characters is difficult. To avoid such problem, we use word models instead of character models. Besides, this technique does not need a large amount of training samples: prepared text samples are converted to image samples which are used to construct individual word models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ding, X., et al.: Character Recognition: Principles Methods and Practice. Tsinhua University Press (2017)
Wang, H., Ding, X.: Multi-font multi-typeface printing Uyghur character recognition. J. Tsinghua Univ. 44(7), 946–949 (2004)
Jin, J., Wang, H., Ding, X., Peng, L.: Printed Arabic document recognition system. In: DDR2005, pp. 48–55 (2005)
Arzigul, H.: Research and development of multi-font printing Uyghur character recognition system. Chin. J. Comput. 11,1480–1484 (2003)
Kadier, N., Peng, L.: A method of Uyghur and Arabic recognition based on HMM and statistical language model. Comput. Appl. Softw. 32(1), 171–174 (2015)
Naz, S., et al.: The optical character recognition of Urdu-like cursive scripts. Pattern Recognit. 47(3), 1229–1248 (2014)
Al-Shatnawi, A.M., et al.: Skeleton extraction: comparison of five methods on the Arabic IFN/ENIT database. In: 2014 6th International Conference on Computer Science and Information Technology (CSIT), pp. 50–59 (2014)
Maqqor, A., et al.: Using HMM toolkit (HTK) for recognition of Arabic manuscripts characters. In: 2014 International Conference on Multimedia Computing and Systems (ICMCS) (2014)
Ahmad, I., Fink, G.A., Mahmoud, S.A.: Improvements in sub-character HMM model based Arabic text recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR) (2014)
Jiang, Z., Ding, X., Peng, L., Liu, C.: Modified bootstrap approach with state number optimization for hidden markov model estimation in small-size printed arabic text line recognition. In: Perner, P. (ed.) MLDM 2014. LNCS (LNAI), vol. 8556, pp. 437–441. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08979-9_33
Ait-Mohand, K., Paquet, T., Ragot, N.: Combining structure and parameter adaptation of HMMs for printed text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(9), 1716–1732 (2014)
Moysset, B., et al.: The A2iA multi-lingual text recognition system at the second maurdor evaluation. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR) (2014)
Mamat, H., Xiaojiao, C.: A method for printed Uyghur character segmentation. In: Liu, C.-L., Zhang, C., Wang, L. (eds.) CCPR 2012. CCIS, vol. 321, pp. 539–547. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33506-8_66
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Meimaiti, H. (2018). A Study on the Printed Uyghur Script Recognition Technique Using Word Visual Features. 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_76
Download citation
DOI: https://doi.org/10.1007/978-3-319-97909-0_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97908-3
Online ISBN: 978-3-319-97909-0
eBook Packages: Computer ScienceComputer Science (R0)