Advertisement

A Comprehensive Study on Character Segmentation

  • Sourabh Sagar
  • Sunanda Dixit
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In identifying the characters from a given image, character segmentation plays an important role. In a given line of text, first, we have to segment the words. Then, in each word there will be a character-by-character segmentation. There have been some rapid developments in this area. Many algorithms have been implemented to increase the accuracy range and decrease the word error rate. This paper aims to provide a review of some of the developments that have happened in this domain.

Keywords

Binarization Covariance Hidden Markov model Hough lines Normalization Optical character recognition 

References

  1. 1.
    Manjunath AE, Sharath B (2013) Implementing Kannada optical character recognition on the android operating system for Kannada sign boards. IJARCCE (Int J Adv Res Comput Commun Eng) 2(1)Google Scholar
  2. 2.
    Zhu B, Shivram A, Setlur S, Govindaraju V, Nakagawa M (2013) Online handwritten cursive word recognition using segmentation-free MRF in combination with P2DBMN-MQDF. IEEE, pp 349–353Google Scholar
  3. 3.
    Kozielski M, Forster J, Ney H (2012) Moment-based image normalization for handwritten text recognition. IEEE, pp 256–261Google Scholar
  4. 4.
    Wu X, Tang Y, Bu W (2014) Offline text-independent writer identification based on scale invariant feature transform. IEEE 9(3)CrossRefGoogle Scholar
  5. 5.
    Tan J, Lai JH, Wang CD, Wang WX, Zuo XX (2012) A new handwritten character segmentation method based on nonlinear clustering. Elsevier, pp 213–219Google Scholar
  6. 6.
    Ghosh SK, Valveny E (2015) Query by string word spotting based on character Bi-Gram indexing. IEEE, pp 881–885Google Scholar
  7. 7.
    Naveena C, Manjunath Aradhya VN (2012) Handwritten character segmentation for Kannada scripts. IEEE, pp 144–149Google Scholar
  8. 8.
    Haji M, Sahoo KA, Bui TD, Suen CY, Ponson D (2012) Statistical hypothesis testing for handwritten word segmentation algorithms. IEEE, pp 114–119Google Scholar
  9. 9.
    Samanta O, Roy A, Bhattacharya U, Parui SK (2015) Script independent online handwriting recognition. IEEE, pp 1251–1255Google Scholar
  10. 10.
    Huang L, Yin F, Chen Q-H, Liu C-L (2013) Keyword spotting in unconstrained handwritten Chinese documents using contextual word model. Image Vis Comput 31:958–968CrossRefGoogle Scholar
  11. 11.
    Lawgali A, Angelova M, Bouridane A (2013) HACDB: handwritten Arabic characters database for automatic character recognition. IEEEGoogle Scholar
  12. 12.
    Sharma OP, Ghose MK, Shah KB (2012) An improved zone based hybrid feature extraction model for handwritten alphabets recognition using euler number. IJSCE (Int J Soft Comput Eng) 2(2). ISSN: 2231-2307Google Scholar
  13. 13.
    Saba T, Rehman A, Sulong G (2011) Cursive script segmentation with neural confidence. IJIC (Int J Innov Comput) 7(8)Google Scholar
  14. 14.
    Marti UV, Bunke H (1998) A full English sentence database for off-line handwriting recognition. IEEEGoogle Scholar
  15. 15.
    Prasanna K, Ramakhanth Kumar P, Thungamani M, Koli M (2011) Kannada text extraction from images and videos for vision impaired persons. IJAET (Int J Adv Eng Technol). ISSN: 2231-1963Google Scholar
  16. 16.
    Sharma S, Sharma R (2016) Character recognition using image processing. IJAETMAS (Int J Adv Eng Technol Manage Appl Sci) 03(09):115–122. ISSN:2349-3224Google Scholar
  17. 17.
    Sandhya N, Krishanan R (2016) Broken Kannada character recognition—a neural network based approach. In: ICEEOT (International conference on electrical, electronics and optimization techniques)Google Scholar
  18. 18.
    Pardeshi R, Hangarge M, Doddamani S, Santosh KC (2016) Handwritten and machine printed text separation from Kannada document images. IEEEGoogle Scholar
  19. 19.
    Banumathi KL, Jagadeesh Chandra AP (2016) Line and word segmentation of Kannada handwritten text documents using projection profile technique. In: ICEECCOT (International conference on electrical, electronics, communication, computer and optimization techniques)Google Scholar
  20. 20.
    Patil MM, Hanni AR (2016) Handwritten Kannada document image processing using optical character recognition. IOSR-JCE (IOSR J Comput Eng) 18(4), Ver. VI:39–47. e-ISSN:2278-0661, p-ISSN:2278-8727Google Scholar
  21. 21.
    Dixit S, Hosahalli Narayan S, Belur M (2014) Kannada text line extraction based on energy minimization and skew correction. In: IEEE IACC (International advance computing conference)Google Scholar
  22. 22.
    Dixit S, Suresh HN (2013) South Indian tamil language handwritten document text line segmentation technique with aid of sliding window and skewing operations. JATiT (J Theoret Appl Inf Technol) 58(2). ISSN: 1992-8645, E-ISSN: 1817-3195Google Scholar
  23. 23.
    Dixit S, Suresh HN (2014) Sliding window technique for handwritten text line segmentation. IJRCEE (Int J Res Comput Eng Electron) 3(04). ISSN: 2319-376XGoogle Scholar
  24. 24.
    Dixit S, Sneha NU, Suresh HN (2014) Text line segmentation of handwritten documents in Hindi and English. IJRITCC (Int J Recent Innov Trends Comput Commun) 02(04). ISSN: 2321-8169Google Scholar
  25. 25.
    Dixit S, Narayan SH (2014) Segmentation of Kannada handwritten text line through computation of variance. IJCSIS (Int J Comput Sci Inf Sec) 12(02). ISSN: 1947-5500Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringDayananda Sagar College of EngineeringBangaloreIndia

Personalised recommendations