Skip to main content
SpringerLink
Log in

Odia character recognition: a directional review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Character recognition is one of the challenging tasks of pattern recognition and machine learning arena. Though a level of saturation has been obtained in machine printed character recognition, there still remains a void while recognizing handwritten scripts. We, in this paper, have summarized all the existing research efforts on the recognition of printed as well as handwritten Odia alphanumeric characters. Odia is a classical and popular language in the Indian subcontinent used by more than 50 million people. In spite of its rich history, popularity and usefulness, not much research efforts have been made to achieve human level accuracy in case of Odia OCR. This review is expected to serve a benchmark reference for research on Odia character recognition and inspire OCR research communities to make tangible impact on its growth. Here several preprocessing methodologies, segmentation approaches, feature extraction techniques and classifier models with their respective accuracies so far reported are critically reviewed, evaluated and compared. The shortcomings and deficiencies in the current state-of-the-art are discussed in detail for each stage of character recognition. A new handwritten alphanumeric character database for Odia is created and reported in this paper in order to address the paucity of benchmark Odia database. From the existing research work, future research paradigms on Odia character recognition are suggested. We hope that such a comprehensive survey on Odia character recognition will serve its purpose of being a solid reference and help creating high accuracy Odia character recognition systems.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Arica N, Yarman-Vural FT (2001) An overview of character recognition focused on off-line handwriting. IEEE Trans Syst Man Cybern Part C Appl Rev 31(2):216–233

    Article  Google Scholar 

  • Bag S, Harit G (2013) A survey on optical character recognition for Bangla and Devanagari scripts. Sadhana 38(1):133–168

    Article  Google Scholar 

  • Bag S, Harit G (2013) A survey on optical character recognition for Bangla and Devanagari scripts. Sadhana 38(1):133–168

    Article  Google Scholar 

  • Bhattacharya U, Chaudhuri B B (2005) Databases for research on recognition of handwritten characters of Indian scripts. In: Proceedings of IEEE 8th international conference on document analysis and recognition ICDAR, pp 789–793

  • Bhattacharya U, Chaudhuri BB (2009) Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Trans Pattern Anal Mach Intell 31(3):444–457

    Article  Google Scholar 

  • Bhowmik TK, Parui SK, Bhattacharya U, Shaw B (2006) An HMM based recognition scheme for handwritten Oriya numerals. In: Proceedings of IEEE 9th international conference on information technology, pp 105–110

  • Biswas S, Mohanty S, Mishra SP (2009) A hybrid Oriya named entity recognition system: integrating HMM with MaxEnt. In: Proceedings of IEEE 2nd international conference on emerging trends in engineering and technology (ICETET), pp 639–643

  • Chanda S, Franke K, Pal U (2012) Text independent writer identification for Oriya script. In: IEEE 10th IAPR international workshop on document analysis systems, pp 369–373

  • Chaudhuri BB, Pal U, Mitra M (2002) Automatic recognition of printed Oriya script. Sadhana 27(1):23–34

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Dash KS, Puhan NB, Panda G (2014) A hybrid feature and discriminant classifier for high accuracy Odia handwritten numeral recognition. In: Proceedings of IEEE region 10 technical symposium (TENSYMP’14) Kuala Lumpur, pp 531–535

  • Dash KS, Puhan NB, Panda G (2014) Non-redundant Stockwell transform based feature extraction for handwritten digit recognition. In: Proceedings of IEEE signal processing and communication, SPCOM’14, pp 1-4

  • Dash KS, Puhan NB, Panda G (2015) Gestalt configural superiority effect: a complexity paradigm for handwritten numeral recognition. In: Proceedings of IEEE 8th international conference on advances in pattern recognition, ICAPR’15, pp 1–6

  • Dash KS, Puhan NB, Panda G (2015) On extraction of features for handwritten Odia numeral recognition in transformed domain. In: Proceedings of IEEE 8th international conference on advances in pattern recognition, ICAPR’15, pp 1–6

  • Dash KS, Puhan NB, Panda G (2015) Handwritten numeral recognition using non-redundant Stockwell transform and bio-inspired optimal zoning. IET Image Process. doi:10.1049/iet-ipr.2015.0146

    Google Scholar 

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558

    Article  MATH  MathSciNet  Google Scholar 

  • Jayadevan R, Kolhe SR, Patil PM, Pal U (2011) Offline recognition of Devanagari script: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):782–796

    Article  Google Scholar 

  • Kimura F, Takashina K, Tsuruoka S, Miyake Y (1987) Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Trans Pattern Anal Mach Intell 1:149–153

    Article  Google Scholar 

  • Kotsiantis SB, Zaharakis ID, Pintelas PE (2006) Machine learning: a review of classification and combining techniques. Artif Intell Rev 26(3):159–190

    Article  Google Scholar 

  • Lee SW, Kim CH, Ma H, Tang YY (1996) Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network. Pattern Recognit 29(12):1953–1961

    Article  Google Scholar 

  • Liu CL, Sako H, Fujisawa H (2004) Discriminative learning quadratic discriminant function for handwriting recognition. IEEE Trans Neural Netw 25(2):430–444

    Article  Google Scholar 

  • Lorigo LM, Govindaraju V (2006) Offline arabic handwriting recognition: a survey. IEEE Trans Pattern Anal Mach Intell 28(5):712–724

    Article  Google Scholar 

  • Majhi B, Mallick J, Rout M (2011) Efficient recognition of Odiya numerals using low complexity neural classifier. In: Proceedings of IEEE international conference on energy, automation, and signal (ICEAS), pp 1–4

  • Meher S, Basa D (2011) An intelligent scanner with handwritten odia character recognition capability. In: Proceedings of IEEE fifth international conference on sensing technology, pp 53–59

  • Mishra TK, Panda S, Majhi B (2013) A comparative analysis of image transformations for handwritten Odia numeral recognition. In: Proceedings of IEEE international conference on advances in computing, communications and informatics, pp 790–793

  • Mohanty S, Dasbebartta HN, Behera TK (2009) An efficient bilingual optical character recognition (English-Oriya) system for printed documents. In: Proceedings of IEEE 7th international conference on advances in pattern recognition, ICAPR’09, pp 398–401

  • Mohapatra RK, Majhi B, Jena SK (2016) Printed Odia digit recognition using finite automaton. In: Proceedings of 3rd international conference on advanced computing, networking and informatics, pp 643–650

  • Nigam S, Khare A (2011) Multifont Oriya character recognition using curvelet transform. In: Information systems for indian languages. Springer, Berlin Heidelberg, pp 150–156

  • Obaidullah M, Mondal A, Roy K (2014) Structural feature based approach or script identification from printed Indian document. In: Proceedings of IEEE international conference on signal processing and integrated networks (SPIN), pp 120–124

  • Pal U, Sharma N, Wakabayashi T, Kimura F (2007) Handwritten numeral recognition of six popular Indian scripts. In: Proceedings of IEEE 9th international conference on document analysis and recognition, ICDAR 2007, vol 2, pp 749–753

  • Pal U, Wakabayashi T, Kimura F (2007) A system for off-line Oriya handwritten character recognition using curvature feature. In: Proceedings of IEEE 10th international conference on information technology (ICIT 2007), pp 227–229

  • Park G-R, Kim I-J, Liu C-L (2013) An evaluation of statistical methods in handwritten Hangul recognition. Int J Doc Anal Recognit 16(3):273–283

    Article  Google Scholar 

  • Parvez MT, Mahmoud SA (2013) Offline Arabic handwritten text recognition: a survey. ACM Comput Surv (CSUR) 45(2):23

    Article  MATH  Google Scholar 

  • Patra PK, Nayak M, Nayak SK, Gobbak NK (2002) Probabilistic neural network for pattern classification. In: Proceeding of IEEE international joint conference on neural networks, IJCNN’02.2, pp 1200–1205

  • Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84

    Article  Google Scholar 

  • Roy K, Pal T, Pal U, Kimura F (2005) Oriya handwritten numeral recognition system. In: Proceedings of IEEE 8th international conference on document analysis and recognition, pp 770–774

  • Senapati D, Rout S, Nayak M (2012) A novel approach to text line and word segmentation on odia printed documents. In: Proceedings of IEEE 3rd international conference on computing communication and networking technologies (ICCCNT), pp 1–6

  • Shioyama T, Wu HY, Nojima T (2001) Recognition algorithm based on wavelet transform for handprinted Chinese characters. In: Proceedings of 14th international conference on pattern recognition, vol 1, pp 229–232

  • Tripathy N, Pal U (2004) Handwriting segmentation of unconstrained Oriya text. In: Proceedings of IEEE 9th international workshop on Frontiers in handwriting recognition, IWFHR-9, pp 306–311

  • Tripathy N, Pal U (2006) Handwriting segmentation of unconstrained Oriya text. Sadhana 31(6):755–769

    Article  Google Scholar 

  • Wakabayashi T, Pal U, Kimura F, Miyake Y (2009) F-ratio based weighted feature extraction for similar shape character recognition. In: Proceedings of 10th international conference on document analysis and recognition, ICDAR’09, pp 196–200

  • Wang SS, Chen PC, Lin WG (1994) Invariant pattern recognition by moment Fourier descriptor. Pattern Recognit. 27:1735–1742

    Article  Google Scholar 

  • Zhu X, Shi Y, Wang S (1999) A new algorithm of connected character image based on Fourier transform. In: Proceedings of 5th international conference on document analysis and recognition, Bangalore, India, pp 788–791

Download references

Author information

Authors and Affiliations

  1. School of Electrical Sciences, Indian Institute of Technology (IIT) Bhubaneswar, Bhubaneswar, Odisha, India

    Kalyan S. Dash, N. B. Puhan & G. Panda

Authors
  1. Kalyan S. Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. B. Puhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kalyan S. Dash.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dash, K.S., Puhan, N.B. & Panda, G. Odia character recognition: a directional review. Artif Intell Rev 48, 473–497 (2017). https://doi.org/10.1007/s10462-016-9507-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-016-9507-5

Keywords

Search

Navigation