Extraction and Identification of Manipuri and Mizo Texts from Scene and Document Images

  • Loitongbam Sanayai MeeteiEmail author
  • Thoudam Doren Singh
  • Sivaji Bandyopadhyay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)


The content inside an image is exceptionally compelling. As such, text within an image can be of special interest and compared to other semantic contents, it tends to be effectively extracted. Text detection within an image is the task of detecting and localizing the portion of an image that contains the text information. Manipuri and Mizo are respectively the lingua francas of two neighboring northeastern states of Manipur and Mizoram in India. While Manipuri, is currently written using Meetei Mayek script and Bengali script, Mizo is written in Roman script with circumflex accent added to the vowels. In this work, we report the task of text detection in natural scene images and document images in Manipuri and Mizo. We made a comparative study between Maximally Stable Extremal Regions (MSER) coupled with Stroke Width Transform (SWT) and Efficient and Accurate Scene Text Detector (EAST) for the text detection. The detected text portion of both the languages is subjected to Optical Character Recognition (OCR) and a post OCR processing of spelling correction. In our experiment of the text detection, EAST outperformed the other method.


Text detection SWT MSER EAST OCR Manipuri Mizo 



This work is supported by Scheme for Promotion of Academic and Research Collaboration (SPARC) Project Code: P995 of No: SPARC/2018-2019/119/SL(IN) under MHRD, Govt of India.


  1. 1.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679–698 (1986)CrossRefGoogle Scholar
  2. 2.
    Cano, J., Pérez-Cortés, J.-C.: Vehicle license plate segmentation in natural images. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 142–149. Springer, Heidelberg (2003). Scholar
  3. 3.
    Chen, D.M., Tsai, S.S., Girod, B., Hsu, C.H., Kim, K.H., Singh, J.P.: Building book inventories using smartphones. In: Proceedings of the 18th ACM international conference on Multimedia, pp. 651–654. ACM (2010)Google Scholar
  4. 4.
    Chen, H., Tsai, S.S., Schroth, G., Chen, D.M., Grzeszczuk, R., Girod, B.: Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 2011 18th IEEE International Conference on Image Processing, pp. 2609–2612. IEEE (2011)Google Scholar
  5. 5.
    Chen, X., Yuille, A.L.: Detecting and reading text in natural scenes. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004, vol. 2, pp. II–II. IEEE (2004)Google Scholar
  6. 6.
    Devi, C.N., Devi, H.M., Das, D.: Text detection from natural scene images for manipuri meetei mayek script. In: 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), pp. 248–251. IEEE (2015)Google Scholar
  7. 7.
    Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2963–2970. IEEE (2010)Google Scholar
  8. 8.
    Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: towards a system for visually impaired persons. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004, ICPR 2004, vol. 2, pp. 683–686. IEEE (2004)Google Scholar
  9. 9.
    Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156–1160. IEEE (2015)Google Scholar
  10. 10.
    Kim, K.I., Jung, K., Kim, J.H.: Color Texture-based object detection: an application to license plate localization. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 293–309. Springer, Heidelberg (2002). Scholar
  11. 11.
    Kim, S.K., Kim, D.W., Kim, H.J.: A recognition of vehicle license plate using a genetic algorithm based segmentation. In: Proceedings of 3rd IEEE International Conference on Image Processing, vol. 2, pp. 661–664. IEEE (1996)Google Scholar
  12. 12.
    Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: Seventh International Conference on Document Analysis and Recognition, 2003, Proceedings, pp. 682–687. Citeseer (2003)Google Scholar
  13. 13.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)CrossRefGoogle Scholar
  14. 14.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  15. 15.
    Özgen, A.C., Fasounaki, M., Ekenel, H.K.: Text detection in natural and computer-generated images. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE (2018)Google Scholar
  16. 16.
    Tsai, S.S., et al.: Mobile product recognition. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1587–1590. ACM (2010)Google Scholar
  17. 17.
    Zhong, Y., Karu, K., Jain, A.K.: Locating text in complex color images. Pattern Recogn. 28(10), 1523–1535 (1995)CrossRefGoogle Scholar
  18. 18.
    Zhou, X., et al.: East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology SilcharSilcharIndia

Personalised recommendations