Advertisement

Toward Recognition and Classification of Hindi Handwritten Document Image

  • Shalini PuriEmail author
  • Satya Prakash Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)

Abstract

With the increased demand of digitization of Indic scripts in today’s world, many Devanagari printed and handwritten text recognition and extraction techniques have been developed and are used in industries, corporate, and institutional domain areas. Because of the script and character structure difficulties, and handwritten content-based criticalities, Hindi handwriting processing is considered as a big bottleneck in recognition systems. This paper introduces NMC handwriting types and complexity evaluators for Hindi language. The inherent challenges of handwriting are discussed further. Although several Hindi-based handwritten character recognizers have been developed, they are limited to the segmentation and identification of character images only. So, this paper proposes a new idea of offline Hindi handwritten document classification, which first recognizes and classifies the character images, and then classifies the document image into the predefined category. In support of this concept, this paper provides a case study using a set of Hindi handwritten documents and shows their segmentation and classification results. The proposed system puts a step ahead in the direction of automatic document image classification.

Keywords

Hindi handwriting Document analysis Character recognition Text identification Template matching Document classification 

References

  1. 1.
    Puri, S., Singh, S.P.: A technical study and analysis of text classification techniques in N-lingual documents. In: International Conference on Computer Communication and Informatics, pp. 1–6. IEEE Press, New York (2016)Google Scholar
  2. 2.
    Toselli, A.H., Juan, A., Vidal, E.: Spontaneous handwriting recognition and classification. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 1, pp. 433–436. IEEE Press, New York (2004)Google Scholar
  3. 3.
    Hassan, E., Garg, R., Chaudhury, S., Gopal, M.: Script based text identification: a multi-level architecture. In: Proceedings of the Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data, pp. 1–8. ACM, New York (2011)Google Scholar
  4. 4.
    Khanduja, D., Nain, N.: Script independent feature set for handwritten text recognition. In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1147–1152. IEEE Press, New York (2014)Google Scholar
  5. 5.
    Ladwani, V.M., Malik, L.: Novel approach to segmentation of handwritten Devnagari word. In: 3rd International Conference on Emerging Trends in Engineering and Technology, pp. 219–224. IEEE Press, New York (2010)Google Scholar
  6. 6.
    Ramachandrula, S., Jain, S., Ravishankar, H.: Offline handwritten word recognition in Hindi. In: Proceeding of the workshop on Document Analysis and Recognition, pp. 49–54. ACM, New York (2012)Google Scholar
  7. 7.
    Garg, N.K., Kaur, L., Jindal, M.K.: A new method for line segmentation of handwritten Hindi text. In: 7th International Conference on Information Technology: New Generations, pp. 392–397. IEEE Press, New York (2010)Google Scholar
  8. 8.
    Thakral, B., Kumar, M.: Devanagari handwritten text segmentation for overlapping and conjunct characters—a proficient technique. In: Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization, pp. 1–4. IEEE Press, New York (2014)Google Scholar
  9. 9.
    Bag, S., Krishna, A.: Character segmentation of Hindi unconstrained handwritten words. In: Barneva, R., Bhattacharya, B., Brimkov, V. (eds.) International Workshop on Combinatorial Image Analysis. Lecture Notes in Computer Science, vol. 9448, pp. 247–260. Springer, Cham (2015)CrossRefGoogle Scholar
  10. 10.
    Mukherji, P., Rege, P.P.: Shape feature and fuzzy logic based offline Devnagari handwritten optical character recognition. J. Pattern Recognit. Res. 5(1), 52–68 (2010)CrossRefGoogle Scholar
  11. 11.
    Pal, U., Wakabayashi, T., Kimura, F.: Comparative study of Devnagari handwritten character recognition using different feature and classifiers. In: 10th International Conference on Document Analysis and Recognition, pp. 1111–1115. IEEE Press, New York (2009)Google Scholar
  12. 12.
    Ghosh, D., Dube, T., Shivaprasad, A.: Script recognition-a review. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2142–2161 (2010)CrossRefGoogle Scholar
  13. 13.
    Verma, G.K., Prasad, S., Kumar, P.: Handwritten Hindi character recognition using curvelet transform. In: Singh, C., Singh, Lehal G., Sengupta, J., Sharma, D.V., Goyal, V. (eds.) Information Systems for Indian Languages. Communications in Computer and Information Science, vol. 139, pp. 224–227. Springer, Berlin (2011)CrossRefGoogle Scholar
  14. 14.
    Sahu, N., Raman, N.K.: An efficient handwritten Devnagari character recognition system using neural network. In: International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing, pp. 173–177. IEEE Press, New York (2013)Google Scholar
  15. 15.
    Gaur, A., Yadav, S.: Handwritten Hindi character recognition using K-means clustering and SVM. In: 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, pp. 65–70. IEEE Press, New York (2015)Google Scholar
  16. 16.
    Singh, D., Saini, J.P., Chauhan, D.S.: Hindi character recognition using RBF neural network and directional group feature extraction technique. In: International Conference on Cognitive Computing and Information Processing, pp. 1–4. IEEE Press, New York (2015)Google Scholar
  17. 17.
    Shelke, S., Apte, S.: A fuzzy based classification scheme for unconstrained handwritten Devanagari character recognition. In: International Conference on Communication, Information & Computing Technology, pp. 1–6. IEEE Press, New York (2015)Google Scholar
  18. 18.
    Bhalerao, M., Bonde, S., Nandedkar, A., Pilawan, S.: Combined classifier approach for offline handwritten Devanagari character recognition using multiple features. In: Hemanth, D., Smys, S. (eds.) Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 45–54. Springer, Cham (2018)CrossRefGoogle Scholar
  19. 19.
    Smith, T.L.: Six basic factors in handwriting classification. J. Crim. Law Criminol. 44(6), 810–816 (1954)CrossRefGoogle Scholar
  20. 20.
    Chemin, A.: Handwriting vs typing: is the pen still mightier than the keyboard? https://www.theguardian.com/science/2014/dec/16/cognitive-benefits-handwriting-decline-typing. Last accessed 21 July 2018
  21. 21.
    Schofield, J.: How can I convert my handwritten notes into word documents? https://www.theguardian.com/technology/askjack/2014/dec/18/how-can-i-convert-my-handwritten-notes-into-word-documents. Last accessed 21 July 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceBITRanchiIndia

Personalised recommendations