Skip to main content
SpringerLink
Log in

A Comprehensive Survey on Handwritten Gujarati Character and Its Modifier Recognition Methods

  • Conference paper
  • First Online:
Information and Communication Technology for Competitive Strategies (ICTCS 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 191))

Abstract

In India, handwritten character recognition is becoming necessity regionalwise due to new education policy 2020. Various technologies are applied to solve the problem in this area like statistical or probability model, support vector machine, Bayes probability model, deterministic finite automaton (DFA), hidden Markov model, and many more which are used. Due to the advancement in machine learning, convolutional neural network is a good solution of HCR which gives more promising results but any new algorithm in machine learning that depends on training data, mathematical function, loss function, and method of evaluation of model. Focusing on past research of handwritten Gujarati character recognition is found that sufficient research is required for modifier level called “Barakshari”. Results obtained in past are limited to character level only. In this paper, our effort is to analyze and summarize previous contributions in the handwritten character recognition for several Indian languages.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Patel, C., Desai, A.: Zone identification for Gujarati handwritten word. In: Proceedings of the 2nd International Conference on Emerging Applications of Information Technology. EAIT 2011, pp. 194–197 (2011)

    Google Scholar 

  2. Mj, B., Kv, K., Me, J.: Comparison of classifiers for gujarati numeral recognition. Int. J. Mach. Intell. 3, 160–163 (2011)

    Article  Google Scholar 

  3. Harish, B.S., Rangan, R.K.: A comprehensive survey on Indian regional language processing. SN Appl. Sci. 2 (2020)

    Google Scholar 

  4. Purohit, A., Chauhan, S.S.: A literature survey on handwritten character recognition. IARJSET 7, 1–5 (2016)

    Google Scholar 

  5. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6 (2019)

    Google Scholar 

  6. Priya, A., Mishra, S., Raj, S., Mandal, S., Datta, S.: Online and offline character recognition: a survey. In: International Conference on Communication and Signal Processing. ICCSP 2016, pp. 967–970 (2016)

    Google Scholar 

  7. Impedovo, D., Pirlo, G.: Zoning methods for handwritten character recognition: a survey. Pattern Recognit. 47, 969–981 (2014)

    Article  Google Scholar 

  8. Sahu, V.L., Kubde, B.: Offline handwritten character recognition techniques using neural network: a review. Int. J. Sci. Eng. Res. 1, 87–94 (2013)

    Google Scholar 

  9. Muthuraman, V.: A study on text recognition using image processing with datamining techniques. Int. J. Comput. Sci. Eng. Open Access (2019)

    Google Scholar 

  10. Dave, N.: Segmentation methods for hand written character recognition. Int. J. Signal Process. Image Process. Pattern Recognit. 8, 155–164 (2015)

    Google Scholar 

  11. Dixit, S., Suresh, H.N.: South Indian Tamil language handwritten document text line segmentation technique with aid of sliding window and skewing operations. J. Theor. Appl. Inf. Technol. 58, 430–439 (2013)

    Google Scholar 

  12. Shah, L. et al.: Rotation estimation of Gujarati script document using hough transform (2014)

    Google Scholar 

  13. Kumar, M., Jindal, M.K., Sharma, R.K.: A novel technique for line segmentation in offline handwritten Gurmukhi script documents. Natl. Acad. Sci. Lett. 40, 273–277 (2017)

    Article  MathSciNet  Google Scholar 

  14. Akter, N., Hossain, S., Islam, M.T., Sarwar, H.: An algorithm for segmenting modifiers from Bangla text. In: Proceedings of the 11th International Conference on Computer and Information Technology. ICCIT 2008, pp. 177–182 (2008)

    Google Scholar 

  15. Pramanik, R., Bag, S., Kumar, R.: A fuzzy and contour-based segmentation methodology for handwritten Hindi words in legal documents. In: Proceedings of the 4th IEEE International Conference on Recent Advances in Information Technology. RAIT 2018, pp. 1–6 (2018)

    Google Scholar 

  16. Pareek, J., Singhania, D., Kumari, R.R., Purohit, S.: Gujarati handwritten character recognition from text images. Proc. Comput. Sci. 171, 514–523 (2020)

    Article  Google Scholar 

  17. Prasad, J.R., Kulkarni, U.V., Prasad, R.S.: Offline handwritten character recognition of Gujrati script using pattern matching. In: 2009 3rd International Conference on Anti-Counterfeiting, Security, and Identification in Communication. ASID 2009 (2009)

    Google Scholar 

  18. Kasthuri, M., Sivaprasatham, V.: Self-learning based cognitive reading and character recognition in image processing techniques. SN Comput. Sci. 1, 1–12 (2020)

    Article  Google Scholar 

  19. Carbune, V., et al.: Fast multi-language LSTM-based online handwriting recognition. Int. J. Doc. Anal. Recognit. 23, 89–102 (2020)

    Article  Google Scholar 

  20. Wu, J.W., Yin, F., Zhang, Y.M., Zhang, X.Y., Liu, C.L.: Handwritten mathematical expression recognition via paired adversarial learning. Int. J. Comput. Vis. (2020)

    Google Scholar 

  21. Ali, S., et al.: An efficient and improved scheme for handwritten digit recognition based on convolutional neural network. SN Appl. Sci. 1, 1–9 (2019)

    Google Scholar 

  22. Ali, H., Ullah, A., Iqbal, T., Khattak, S.: Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network. SN Appl. Sci. 2, 1–12 (2020)

    Google Scholar 

  23. Gatto, B.B., dos Santos, E.M., Fukui, K., Júnior, W.S.S., dos Santos, K.V.: Fukunaga–Koontz convolutional network with applications on character classification. Neural Process. Lett. (2020)

    Google Scholar 

  24. Demilew, F.A., Sekeroglu, B.: Ancient Geez script recognition using deep learning. SN Appl. Sci. 1, 1–7 (2019)

    Article  Google Scholar 

  25. Liu, Z., Pan, X., Peng, Y.: Character recognition algorithm based on fusion probability model and deep learning. Comput. J. 00 (2020)

    Google Scholar 

  26. John, J., Pramod, K.V., Balakrishnan, K.: Offline handwritten Malayalam character recognition based on chain code histogram. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology. ICETECT 2011, pp. 736–741 (2011)

    Google Scholar 

  27. Rahman, M.M., Islam, M.S., Sassi, R., Aktaruzzaman, M.: Convolutional neural networks performance comparison for handwritten Bengali numerals recognition. SN Appl. Sci. 1, 1–11 (2019)

    Google Scholar 

  28. Sharma, A., Thakkar, P., Adhyaru, D., Zaveri, T.: Features fusion based approach for handwritten Gujarati character recognition. Nirma Univ. J. Eng. Technol. (2017)

    Google Scholar 

  29. Singh, P., Budhiraja, S.: Offline handwritten Gurmukhi numeral recognition using wavelet transforms. Int. J. Mod. Educ. Comput. Sci. 4, 34–39 (2012)

    Article  Google Scholar 

  30. Desai, A.A.: Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space. CSI Trans. ICT 2, 235–241 (2015)

    Article  Google Scholar 

  31. Farkya, S., Surampudi, G., Kothari, A.: Hindi speech synthesis by concatenation of recognized hand written Devnagri script using support vector machines classifier. In: 2015 International Conference on Communication and Signal Processing. ICCSP 2015, pp. 893–898 (2015)

    Google Scholar 

  32. Gupta, A., Srivastava, M., Mahanta, C.: Offline handwritten character recognition using neural network. In: ICCAIE 2011—2011 IEEE Conference on Computer Applications and Industrial Electronics, pp. 102–107 (2011)

    Google Scholar 

  33. Maloo, M., Kale, K.V.: Support vector machine based Gujarati numeral recognition. Int. J. Comput. Sci. Eng. (IJCSE) 3, 2595–2600 (2011). ISSN 0975-3397

    Google Scholar 

  34. Nagar, R., Mitra, S.K.: Feature extraction based on stroke orientation estimation technique for handwritten numeral. In: ICAPR 2015—2015 8th International Conference on Advances in Pattern Recognition (2015)

    Google Scholar 

  35. Desai, A.A.: Gujarati handwritten numeral optical character reorganization through neural network. Pattern Recognit. 43, 2582–2589 (2010)

    Article  Google Scholar 

  36. Goswami, M., Mitra, S.: Structural feature based classification of printed Gujarati characters. In: Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 8251, pp. 82–87 (2013)

    Google Scholar 

  37. Gohel, C.C., Goswami, M.M., Prajapati, V.K.: On-line handwritten Gujarati character recognition using low level stroke. In: Proceedings of the 2015 3rd International Conference on Image Information Processing. ICIIP 2015, pp. 130–134 (2016)

    Google Scholar 

  38. Thaker, H.R., Kumbharana, C.K.: Structural feature extraction to recognize some of the offline isolated handwritten Gujarati characters using decision tree classifier. Int. J. Comput. Appl. 99, 46–50 (2014)

    Google Scholar 

  39. Madushanka, P.T.C., Bandara, R., Ranathunga, L.: Sinhala handwritten character recognition by using enhanced thinning and curvature histogram based method. In: 2017 IEEE 2nd International Conference on Signal Image Processing. ICSIP 2017, Jan 2017, pp. 46–50 (2017)

    Google Scholar 

  40. Macwan, S.J., Vyas, A.N.: Classification of offline Gujarati handwritten characters. In: 2015 International Conference on Advances in Computing, Communications and Informatics. ICACCI 2015, pp. 1535–1541 (2015)

    Google Scholar 

  41. Álvarez, D., Fernández, R., Sánchez, L.: Stroke-based intelligent character recognition using a deterministic finite automaton. Log. J. IGPL 23, 463–471 (2014)

    Article  MathSciNet  Google Scholar 

  42. Álvarez, D., Fernández, R.A., Sánchez, L.: Fuzzy system for intelligent word recognition using a regular grammar. J. Appl. Log. 24, 45–53 (2017)

    Article  MathSciNet  Google Scholar 

  43. Joshi, V., Panday, S.P.: Character component segmentation and categorization of machine printed text in Devanagari (Nepali) script in digital image processing. In: Proceedings of the 2018 IEEE 3rd International Conference on Computing, Communication and Security. ICCCS 2018, pp. 191–198 (2018)

    Google Scholar 

  44. Nopsuwanchai, R., Povey, D.: Discriminative training for HMM-based offline handwritten 2. In: Maximum Mutual Information Training of Analysis (2003)

    Google Scholar 

  45. Das, R.L., Binod, I., Prasad, K., Sanyal, G.: HMM based offline handwritten writer independent english character recognition using global and local feature extraction. Int. J. Comput. Appl. 46, 975–8887 (2012)

    Google Scholar 

  46. Garg, N.K., Kaur, L., Jndal, M.: Recognition of offline handwritten hindi text using middle zone of the words. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science. ICIS 2015—Proceedings, pp. 325–328 (2015)

    Google Scholar 

  47. Obaidullah, S.M., Halder, C., Santosh, K.C., Das, N., Roy, K.: PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimed. Tools Appl. 77, 1643–1678 (2018)

    Article  Google Scholar 

  48. Yousaf, A., Khan, M.J., Imran, M., Khurshid, K.: Benchmark dataset for offline handwritten character recognition. Proceedings of the 2017 13th International Conference on Emerging Technologies. ICET2017, Jan 2018, pp. 1–5 (2018)

    Google Scholar 

  49. Nehra, M.S., Nain, N., Ahmed, M.: Benchmarking of text segmentation in devnagari handwritten document. In: 2016 IEEE 7th Power India International Conference. PIICON 2016, pp. 0–3 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Atmiya University, Rajkot, Gujarat, 360005, India

    Priyank D. Doshi

  2. Shree M. & N. Virani Science College, Rajkot, Gujarat, 360005, India

    Pratik A. Vanjara

Authors
  1. Priyank D. Doshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pratik A. Vanjara
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

  2. Computing and Technology, Nottingham Trent University, Nottingham, Nottinghamshire, UK

    Mufti Mahmud

  3. University of Peradeniya, Kandy, Sri Lanka

    Roshan G. Ragel

  4. Prof Ram Meghe College of Engineering and Management, Amravati, India

    Nileshsingh V. Thakur

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Doshi, P.D., Vanjara, P.A. (2022). A Comprehensive Survey on Handwritten Gujarati Character and Its Modifier Recognition Methods. In: Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-16-0739-4_79

Download citation

Publish with us

Policies and ethics

Search

Navigation