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Character and numeral recognition for non-Indic and Indic scripts: a survey

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Abstract

A collection of different scripts is employed in writing languages throughout the world. Character and numeral recognition of a particular script is a key area in the field of pattern recognition. In this paper, we have presented a comprehensive survey on character and numeral recognition of non-Indic and Indic scripts. Many researchers have done work on character and numeral recognition from the most recent couple of years. In perspective of this, few strategies for character/numeral have been developed so far. There are an immense number of frameworks available for printed and handwritten character recognition for non-Indic scripts. But, only a limited number of systems are offered for character/numeral recognition of Indic scripts. However, few endeavors have been made on the recognition of Bangla, Devanagari, Gurmukhi, Kannada, Oriya and Tamil scripts. In this paper, we have additionally examined major challenges/issues for character/numeral recognition. The efforts in two directions (non-Indic and Indic scripts) are reflected in this paper. When compared with non-Indic scripts, the research on character recognition of Indic scripts has not achieved that perfection yet. The techniques used for recognition of non-Indic scripts may be used for recognition of Indic scripts (printed/handwritten text) and vice versa to improve the recognition rates. It is also noticed that the research in this field is quietly thin and still more research is to be done, particularly in the case of handwritten Indic scripts documents.

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References

  • Abd MA, Paschos G (2007) Effective Arabic character recognition using support vector machines. Innov Adv Tech Comput Inf Sci Eng 7–11

  • Acharya D, Reddy NVS, Makkithaya K (2008) Multilevel classifiers in recognition of handwritten Kannada numerals. Proc World Acad Sci Eng Technol (WASET) 18:274–279

    Google Scholar 

  • Afroge S, Ahmed B, Mahmud F (2016) Optical character recognition using back propagation neural network. In: Proceedings of the 2nd international conference on electrical, computer and telecommunication engineering (ICECTE), pp 1–4

  • Ajmire PE, Warkhede SE (2010) Handwritten Marathi character (vowel) recognition. Adv Inf Min 2(2):11–13

    Google Scholar 

  • Alaei A, Pal U, Nagabhushan P (2009) Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals. In: Proceedings of the 7th international conference on advances in pattern recognition (ICAPR), pp 391–394

  • Alaei A, Nagabhushan P, Pal U (2010a) A baseline dependent approach for Persian handwritten character segmentation. In: Proceedings of the 20th international conference on pattern recognition (ICPR), pp 1977–1980

  • Alaei A, Nagabhushan P, Pal U (2010b) A new two-stage scheme for the recognition of Persian handwritten characters. In: Proceedings of the 12th international conference on frontiers in handwriting recognition (ICFHR), pp 130–135

  • Almuallim H, Yamaguchi S (1987) A method of recognition of Arabic cursive handwriting. IEEE Trans Pattern Anal Mach Intell 9(5):715–722

    Article  Google Scholar 

  • Althobaiti H, Lu C (2017) A survey on Arabic optical character recognition and an isolated handwritten Arabic character recognition algorithm using encoded freeman chain code. In: Proceedings of the 51st annual conference on information sciences and systems

  • Antani S, Agnihotri L (1999) Gujarati character recognition. In: Proceedings of the 5th international conference on document analysis and recognition (ICDAR), pp 418–421

  • Aparna KH, Subramanian V, Kasirajan M, Prakash GV, Chakravarthy VS, Madhvanath S (2004) Online handwriting recognition for Tamil. In: Proceedings of the 9th international workshop on frontiers in handwriting recognition (IWFHR), pp 438–443

  • Ardeshana M, Sharma AK, Adhyaru DM, Zaveri TH (2016) Handwritten Gujarati character recognition based on discrete cosine transform. In: Proceedings of the IRF-IEEE forum international conference, pp 23–26

  • Arora A, Namboodiri AM (2010) A hybrid model for recognition of online handwriting in Indian scripts. In: Proceedings of the 12th international conference on frontiers in handwriting recognition (ICFHR), pp 433–438

  • Asavareongchai N, Giarta E (2016) Recognition of Thai characters and text from document templates. Project report, pp 1–7

  • Ashwin TV, Sastry PS (2002) A font and size-independent OCR system for printed Kannada documents using support vector machines. SADHANA 27(1):35–58

    Article  Google Scholar 

  • Baiju KB, Sabeerath K (2016) Online recognition of Malayalam handwritten scripts—a comparison using KNN, MLP and SVM. In: Proceedings of the international conference on advances in computing, communications and informatics, pp 2078–2083

  • Bajaj R, Dey L, Chaudhury S (2002) Devanagari numeral recognition by combining decision of multiple connectionist classifiers. SADHANA 27(1):59–72

    Article  Google Scholar 

  • Bansal V, Sinha RMK (2000) Integrating knowledge sources in Devanagari text recognition system. IEEE Trans Syst Man Cybern Part A 30(4):500–505

    Article  Google Scholar 

  • Belhe S, Paulzagade C, Deshmukh A, Jetley S, Mehrotra K (2012) Hindi handwritten word recognition using HMM and symbol tree. In: Proceedings of the workshop on document analysis and recognition (DAR), pp 9–14

  • Bharath A, Madhvanath S (2011) HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts. IEEE Trans Pattern Anal Mach Intell (PAMI) 34(4):670–682

    Article  Google Scholar 

  • Bhattacharya U, Chaudhuri BB (2003) A majority voting scheme for multi-resolution recognition of handprinted numerals. In: Proceedings of the 7th international conference on document analysis and recognition (ICDAR), pp 16–20

  • Bhattacharya U, Vajda S, Mallick A, Chaudhuri BB, Belaid A (2004) On the choice of training set, architecture and combination rule of multiple MLP classifiers for multi-resolution recognition of handwritten characters. In: Proceedings of the 9th international workshop on frontiers in handwriting recognition (IWFHR), pp 419–424

  • Bhattacharya U, Shridhar M, Parui SK (2006) On recognition of handwritten Bangla characters. In: Proceedings of the international conference on computer vision, graphics and image processing (ICVGIP), pp 817–828

  • Bhattacharya U, Gupta BK, Parui SK (2007) Direction code based features for recognition of online handwritten characters of Bangla. In: Proceedings of the 9th international conference on document analysis and recognition (ICDAR), vol 1, pp 58–62

  • Bhoi S, Dogra DP, Roy PP (2015) Handwritten text recognition in Odia script using hidden Markov model. In: Proceedings of the 5th national conference on computer vision, pattern recognition, image processing and graphics, pp 1–3

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

  • Bishnu A, Chaudhuri BB (1999) Segmentation of Bangla handwritten text into characters by recursive contour following. In: Proceedings of the 5th international conference on document analysis and recognition (ICDAR), pp 402–405

  • Bluche T, Messina R (2016) Faster segmentation-free handwritten Chinese text recognition with character decompositions. In: Proceedings of the 15th international conference on frontiers in handwriting recognition, pp 530–535

  • Borovikov E (2004) A survey of modern optical character recognition techniques. Project report, pp 1–38

  • Bunke H, Varga T (2007) Off-line Roman cursive handwriting recognition. Adv Pattern Recognit 165–183

  • Chanda S, Pal U, Kimura F (2007a) Identification of Japanese and English script from a single document page. In: Proceedings of the 7th IEEE international conference on computer and information technology, pp 656–661

  • Chanda S, Terrades OR, Pal U (2007b) SVM based scheme for Thai and English script identification. In: Proceedings of the 9th international conference on document analysis and recognition (ICDAR), vol 1, pp 551–555

  • Chanda S, Pal S, Franke K, Pal U (2009) Two-stage approach for word-wise script identification. In: Proceedings of the 10th international conference on document analysis and recognition (ICDAR), pp 926–930

  • Chaudhary S, Sharma S, Kumar B (2015) Recognition of printed Oriya script using gradient based features. In: Proceedings of the 2015 annual IEEE India conference, pp 1–5

  • Das S, Banerjee S (2015) An algorithm for Japanese character recognition. Int J Image Gr Signal Process 1:9–15

    Google Scholar 

  • Deepu V, Madhvanath S, Ramakrishnan AG (2004) Principal component analysis for online handwritten character recognition. In: Proceedings of the 17th international conference on pattern recognition (ICPR), vol 2, pp 327–330

  • Desai AA (2010) Gujarati handwritten numeral optical character reorganization through neural network. Pattern Recognit 43(7):2582–2589

    Article  MATH  Google Scholar 

  • Dholakia J, Yajnik A, Negi A (2007) Wavelet feature based confusion character sets for Gujarati script. In: Proceedings of the international conference on computational intelligence and multimedia applications (CIMA), pp 366–370

  • Dutta A, Chaudhury S (1993) Bengali alpha-numeric character recognition using curvature features. Pattern Recognit 26(12):1757–1770

    Article  Google Scholar 

  • Elakkiya V, Muthumani I, Jegajothi M (2017) Tamil text recognition using KNN classifier. Adv Nat Appl Sci 11(7):41–45

    Google Scholar 

  • Garg NK, Kaur L, Jindal MK (2010) A new method for line segmentation of handwritten Hindi text. In: Proceedings of the 7th international conference on information technology: new generations (ITNG), pp 392–397

  • Garain U, Chaudhuri BB, Pal TT (2002) Online handwritten Indian script recognition: a human motor function based framework. In: Proceedings of the 16th international conference on pattern recognition (ICPR), vol 3, pp 164–167

  • Gohell CC, Goswam MM, Prajapate YK (2015) On-line Handwritten Gujarati character recognition using low level stroke. In: Proceedings of the third international conference on image information processing, pp 130–134

  • Grosicki E, Abed H (2009) Handwriting recognition competition. In: Proceedings of the 10th international conference on document analysis and recognition (ICDAR), pp 1398–1402

  • Hanmandlu M, Murthy OVR, Madasu VK (2007) Fuzzy model based recognition of handwritten Hindi characters. In: Proceedings of the 9th biennial conference of the Australian pattern recognition society on digital image computing and techniques and applications, pp 454–461

  • He S, Hu X (2016) Chinese character recognition in natural scenes. In: Proceedings of the 9th international symposium on computational intelligence and design, pp 124–127

  • Hussain R, Raza A, Siddiqi I, Khurshid K, Djeddi C (2015) A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation. EURASIP J Image Video Process 46:1–24

    Google Scholar 

  • Impedovo S, Dimauro G (1990) An interactive system for the selection of handwritten numeral classes. In: Proceedings of the 10th international conference on pattern recognition (ICPR), pp 563–566

  • Izadi S, Sadri J, Solimanpour F, Suen CY (2006) A review on Persian script and recognition techniques. In: Proceedings of the conference on Arabic and Chinese handwriting, pp 22–35

  • Janani G, Vishalini V, Kumar PM (2016) Recognition and analysis of Tamil inscriptions and mapping using image processing techniques. In: Second international conference on science technology engineering and management, pp 181–184

  • Jayadevan R, Pal U, Kimura F (2010) Recognition of words from legal amounts of Indian bank cheques. In: Proceedings of the international conference on frontiers in handwriting recognition (ICFHR), pp 166–171

  • Jindal MK, Lehal GS, Sharma RK (2005) Segmentation problems and solutions in printed degraded Gurmukhi script. Int J Signal Process 2(4):258–267

    Google Scholar 

  • Jindal MK, Sharma RK, Lehal GS (2007) Segmentation of horizontally overlapping lines in printed Indian scripts. Int J Comput Intell Res 3(4):277–286

    Article  Google Scholar 

  • John R, Raju G, Guru DS (2007) 1D wavelet transform of projection profiles for isolated handwritten Malayalam character recognition. Proceedings of the international conference on computational intelligence and multimedia applications (ICCIMA) 2:481–485

    Google Scholar 

  • Joshi N, Sita G, Ramakrishnan AG, Madhvanath S (2004) Tamil handwriting recognition using subspace and DTW based classifiers. In: Proceedings of the international conference on neural information processing (ICONIP), pp 806–813

  • Joshi N, Sita G, Ramakrishnan AG, Deepu V, Madhvanath S (2005) Machine recognition of online handwritten Devanagari characters. In: Proceedings of the 8th international conference on document analysis and recognition (ICDAR), vol 2, pp 1156–1160

  • Jyothi J, Manjusha K, Kumar MA, Soman KP (2015) Innovative feature sets for machine learning based Telugu character recognition. Indian J Sci Technol 8(24):1–7

    Article  Google Scholar 

  • Kacem A, Aouiti N, Belaid A (2012) Structural features extraction for handwritten Arabic personal names recognition. In: Proceedings of the international conference on frontiers in handwriting recognition (ICFHR), pp 268–273

  • Karnchanapusakij C, Suwannakat P, Rakprasertsuk W, Dejdumrong N (2009) Online handwriting Thai character recognition. In: Proceedings of the 6th international conference on computer graphics, imaging and visualization (CGIV), pp 323–328

  • Karthik S, Srikanta MK (2016) Segmentation and recognition of handwritten Kannada text using relevance feedback and histogram of oriented gradients: a novel approach. Int J Adv Comput Sci Appl 7(1):472–476

    Google Scholar 

  • Kinjarapu AA, Yelavarti KC, Valurouthu KP (2016) Online recognition of handwritten Telugu script characters. In: Proceedings of the international conference on signal processing, communication, power and embedded system, pp 426–432

  • Kobchaisawat T, Chalidabhongse T (2015) A method for multi-oriented Thai text localization in natural scene images using convolutional neural network. In: Proceedings of the international conference on signal and image processing applications, pp 220–225

  • Kumar D (2008) AI approach to hand written Devanagri script recognition. In: Proceedings of the IEEE region 10th international conference on EC3-energy, computer, communication and control systems vol 2, pp 229–237

  • Kumar M, Jindal MK, Sharma RK (2011a) Review on OCR for handwritten Indian scripts character recognition. In: Proceedings of the first international conference on digital image processing and pattern recognition (DPPR), Tirunelveli, Tamil Nadu, vol 205, pp 268–276

  • Kumar M, Jindal MK, Sharma RK (2011b) $k$-Nearest neighbor based offline handwritten Gurmukhi character recognition. In: Proceedings of the international conference on image information processing (ICIIP), Jaypee University of Information Technology, Waknaghat (Shimla), pp 1–4

  • Kumar M, Sharma RK, Jindal MK (2011c) Classification of characters and grading writers in offline handwritten Gurmukhi script. In: Proceedings of the international conference on image information processing (ICIIP), Jaypee University of Information Technology, Waknaghat (Shimla), pp 1–4

  • Kumar M, Sharma RK, Jindal MK (2011d) SVM based offline handwritten Gurmukhi character recognition. In: Proceedings of the international workshop on soft computing applications and knowledge discovery (SCAKD), National Research University Higher School of Economics, Moscow (Russia), pp 51–62

  • Kumar M, Jindal MK, Sharma RK (2012) Offline handwritten Gurmukhi character recognition: study of different features and classifiers combinations. In: Proceedings of the workshop on document analysis and recognition (IWDAR), IIT Bombay, pp 94–99

  • Kumar M, Sharma RK, Jindal MK (2013a) A novel feature extraction technique for offline handwritten Gurmukhi character recognition. IETE J Res 59(6):687–692

    Article  Google Scholar 

  • Kumar M, Jindal MK, Sharma RK (2013b) PCA based offline handwritten Gurmukhi character recognition. Smart Comput Rev 3(5):346–357

    Article  Google Scholar 

  • Kumar M, Sharma RK, Jindal MK (2014a) Efficient feature extraction techniques for offline handwritten Gurmukhi character recognition. Natl Acad Sci Lett 37(4):381–391

    Article  Google Scholar 

  • Kumar M, Sharma RK, Jindal MK (2014b) A novel hierarchical technique for offline handwritten Gurmukhi character recognition. Natl Acad Sci Lett 37(6):567–572

    Article  Google Scholar 

  • Kumar M, Jindal MK, Sharma RK (2016) Offline handwritten Gurmukhi character recognition: analytical study of different transformations. Proc Natl Acad Sci India Sect A Phys Sci 87(1):137–143

    Article  Google Scholar 

  • Koundal K, Kumar M, Garg NK (2017) Punjabi optical character recognition: a survey. Indian J Sci Technol 10(19):1–8

    Article  Google Scholar 

  • Kunte RS, Samuel RDS (2007) A simple and efficient optical character recognition system for basic symbols in printed Kannada text. SADHANA 32(5):521–533

    Article  Google Scholar 

  • Lajish VL (2007) Handwritten character recognition using perceptual fuzzy-zoning and class modular neural networks. In: Proceedings of the 4th international conference on innovations in information technology (ICIIT), pp 188–192

  • Lajish VL (2008) Handwritten character recognition using gray-scale based state-space parameters and class modular NN. In: Proceedings of the international conference on signal processing, communications and networking (ICSCN), pp 374–379

  • Lajish VL, Kopparapu SK (2010) Fuzzy directional features for unconstrained on-line Devanagari handwriting recognition. In: Proceedings of the national conference on communications (NCC), pp 1–5

  • Lehal GS, Singh C (1999) Feature extraction and classification for OCR of Gurmukhi script. Vivek 12(2):2–12

    Google Scholar 

  • Lehal GS, Singh C, Lehal R (2001) A shape based post processor for Gurmukhi OCR. In: Proceedings of the 6th international conference on document analysis and recognition (ICDAR), pp 1105–1109

  • Liang J, Zhu B, Kumagai T, Nakagawa M (2016) Character-position-free on-line handwritten Japanese text recognition by two segmentation methods. IEICE Trans Inf Syst E99–D(4):1172–1181

  • Liao SX, Chiang A, Lu Q, Pawlak M (2002) Chinese character recognition via Gegenbauer moments. In: International conference on pattern recognition, pp 485–488

  • Liu J, Ma SP (1996) An overview of printed Chinese character recognition techniques. In: Proceedings of the international conference on Chinese computing, Singapore, pp 325–333

  • Liwicki M, Bunke H (2007) Combining on-line and off-line systems for handwriting recognition. In: Proceedings of the international conference on document analysis and recognition (ICDAR), pp 372–376

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

    Article  Google Scholar 

  • Lu S, Tu X, Lu Y (2008) An improved two-layer SOM classifier for handwritten numeral recognition. In: Proceedings of the international conference on intelligent information technology, pp 367–371

  • Modi H, Parikh MC (2017) A review on optical character recognition techniques. Int J Comput Appl 160(6):20–24

    Google Scholar 

  • Mondal T, Bhattacharya U, Parui SK, Das K, Mandalapu D (2010) On-line handwriting recognition of Indian scripts-the first benchmark. In: Proceedings of the 12th international conference on frontiers in handwriting recognition (ICFHR), pp 200–205

  • Nakagawa M, Zhu B, Onuma M (2005) A model of on-line handwritten Japanese text recognition free from line direction and writing format constraints. IEICE Trans Inf Syst E88–D(8):1815–1822

  • Pal U, Chaudhuri BB (1994) OCR in Bangla: an Indo-Bangladeshi language. In: Proceedings of the 12th international conference on pattern recognition (ICPR), pp 269–274

  • Pal U, Chaudhuri BB (1997) Automatic separation of words in multi-lingual multi-script Indian documents. In: Proceedings of the 4th international conference on document analysis and recognition (ICDAR), vol 2, pp 576–579

  • Pal U, Chaudhuri BB (2001) Automatic identification of English, Chinese, Arabic, Devanagari and Bangla script line. In: Proceedings of the 6th international conference on document analysis and recognition (ICDAR), pp 790–794

  • Pal U, Dutta S (2003) Segmentation of Bangla unconstrained handwritten text. In: Proceedings of the 7th international conference on document analysis and recognition (ICDAR), pp 1128–1132

  • Pal U, Jayadevan R, Sharma N (2012) Handwriting recognition in Indian regional scripts: a survey of offline techniques. ACM Trans Asian Lang Inf Process 11(1):1–35

    Article  Google Scholar 

  • Pal U, Roy K, Kimura F (2006) A lexicon driven method for unconstrained Bangla handwritten word recognition. In: Proceedings of the 10th international workshop on frontiers in handwriting recognition (IWFHR), pp 601–606

  • Pal U, Wakabayashi T, Kimura F (2007a) Handwritten Bangla compound character recognition using gradient feature. In: Proceedings of the 10th international conference on information technology (ICIT), pp 208–213

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

  • Pal U, Sharma N, Wakabayashi T, Kimura F (2007c) Off-line handwritten character recognition of Devanagari script. In: Proceedings of the 9th international conference on document analysis and recognition (ICDAR), pp 496–500

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

  • Pal U, Roy K, Kimura F (2008) Bangla handwritten pin code string recognition for Indian postal automation. In: Proceedings of the 11th international conference on frontiers in handwriting recognition (ICFHR), pp 290–295

  • Pal U, Wakabayashi T, Kimura F (2009) Comparative study of Devanagari handwritten character recognition using different feature and classifiers. In: Proceedings of the 10th international conference on document analysis and recognition (ICDAR), pp 1111–1115

  • Pal U, Roy RK, Kimura F (2010) Bangla and English city name recognition for Indian postal automation. In: Proceedings of the 20th international conference on pattern recognition (ICPR), pp 1985–1988

  • Park J, Govindaraju V, Srihari SN (2000) OCR in a hierarchical feature space. IEEE Trans Pattern Anal Mach Intell (PAMI) 22(4):400–407

    Article  Google Scholar 

  • Pasha S, Padma MC (2015) Handwritten Kannada character recognition using wavelet transform and structural features. In: Proceedings of the international conference on emerging research in electronics, computer science and technology, pp 346–351

  • Patel BC, Kayasth MM (2017) Recognition of offline handwritten Gujarati numerals. I Manag J Inf Technol 6(1):14

    Google Scholar 

  • Prasad JR (2014) Handwritten character recognition: a review. Int J Comput Sci Netw 3(5):340–351

    Google Scholar 

  • Prasad SD, Kanduri Y (2016) Telugu handwritten character recognition using adaptive and static zoning methods. In: Proceedings of the IEEE students technology symposium, pp 299–304

  • Prasad JR, Kulkarni UV, Prasad RS (2009) Offline handwritten character recognition of Gujarati script using pattern matching. In: Proceedings of the 3rd international conference on anti-counterfeiting, security, and identification in communication (ASID), pp 611–615

  • Prasanth L, Babu JV, Sharma RR, Rao PGV, Manadalapu D (2007) Elastic matching of online handwritten Tamil and Telugu scripts using local features. In: Proceedings of the 9th international conference on document analysis and recognition (ICDAR), vol 2, pp 1028–1032

  • Purkait P, Chanda B (2010) Off-line recognition of handwritten Bengali numerals using morphological features. In: Proceedings of the 12th international conference on the frontiers of handwriting recognition (ICFHR), pp 363–368

  • Rahiman MA, Shajan A, Elizabeth A, Divya MK, Kumar GM, Rajasree MS (2010) Isolated handwritten Malayalam character recognition using HLH intensity patterns. In: Proceedings of the 2nd international conference on machine learning and computing (ICMLC), pp 147–151

  • Raj A (2015) An optical character recognition of machine printed oriya script. In: Proceedings of the 3rd international conference on image information processing, pp 543–547

  • Rajashekararadhya SV, Ranjan PV (2008a) Neural network based handwritten numeral recognition of Kannada and Telugu scripts. In: Proceedings of the 10th IEEE international conference on TENCON, pp 1–5

  • Rajashekararadhya SV, Ranjan PV (2008b) Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular South Indian scripts. J Theor Appl Inf Technol 4(12):1171–1181

    Google Scholar 

  • Rajashekararadhya SV, Ranjan PV (2009a) Zone based feature extraction algorithm for handwritten numeral recognition of Kannada script. In: Proceedings of the IEEE international conference on advance computing conference (IACC), pp 525–528

  • Rajashekararadhya SV, Ranjan PV (2009b) Handwritten numeral/mixed numerals recognition of South Indian scripts: the zone based feature extraction method. J Theor Appl Inf Technol 7(1):63–79

    Google Scholar 

  • Rajput GG, Hangarge M (2007) Recognition of isolated handwritten Kannada numerals based on image fusion method. In: Proceedings of the international conference on PReMI, pp 153–160

  • Ramakrishnan AG, Shashidhar J (2013) Development of OHWR system for Kannada. VishwaBharat@tdil 39–40:67–95

  • Rampalli R, Ramakrishnan AG (2011) Fusion of complementary online and offline strategies for recognition of handwritten Kannada characters. J Univers Comput Sci (JUCS) 17(1):81–93

    Google Scholar 

  • Reddy GS, Sharma P, Prasanna SRM, Mahanta C, Sharma LN (2012a) Combined online and offline assamese handwritten numeral recognizer. In: Proceedings of the 18th national conference on communications (NCC-2012), IIT Kharagpur

  • Reddy GS, Sarma B, Naik RK, Prasanna SRM, Mahanta C (2012b) Assamese online handwritten digit recognition system using hidden Markov models. In: Proceedings of the workshop on document analysis and recognition (DAR), pp 108–113

  • Roy K, Pal U (2006) Word-wise hand-written script separation for Indian postal automation. In: Proceedings of the 10th international workshop on frontiers in handwriting recognition (IWFHR), pp 521–526

  • Roy K, Vajda S, Pal U, Chaudhuri BB (2004) A system towards Indian postal automation. In: Proceedings of the 9th international workshop on frontiers in handwriting recognition (IWFHR), pp 580–585

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

  • Sarma B, Mehrotra K, Naik RK, Prasanna SRM, Belhe S, Mahanta C (2013) Handwritten assamese numeral recognizer using HMM and SVM classifiers. In: Proceedings of the 19th national conference on communications (NCC), IIT Delhi

  • Sastry PN, Krishnan R, Ram BVS (2010) Classification and identification of Telugu handwritten characters extracted from palm leaves using decision tree approach. ARPN J Appl Eng Appl Sci 5(3):22–32

    Google Scholar 

  • Sastry PN, Lakshmi TRV, Rao NVK, Rajinikanth TV, Wahab A (2014) Telugu handwritten character recognition using zoning features. In: Proceedings of the international conference on IT convergence and security, pp 1–4

  • Schantz HF (1982) History of OCR, optical character recognition. Recognition Technologies Users Association

  • Schomaker L (2007) Retrieval of handwritten lines in historical documents. In: Proceedings of the 9th international conference on document analysis and recognition (ICDAR), pp 594–598

  • Schomaker L, Segers E (1999) Finding features used in the human reading of cursive handwriting. Int J Doc Anal Recognit (IJDAR) 2:13–18

    Article  Google Scholar 

  • Sethi K, Chatterjee B (1976) Machine recognition of constrained hand-printed Devanagari numerals. J Inst Electr Telecom Eng 22:532–535

    Google Scholar 

  • Shahin AA (2017) Printed Arabic text recognition using linear and nonlinear regression. Int J Adv Comput Syst Appl 8(1):227–235

    Google Scholar 

  • Sharma DV, Lehal GS (2006) An iterative algorithm for segmentation of isolated handwritten words in Gurmukhi script. In: Proceedings of the 18th international conference on pattern recognition (ICPR), vol 2, pp 1022–1025

  • Sharma DV, Lehal GS (2009) Form field frame boundary removal for form processing system in Gurmukhi script. In: Proceedings of the 10th international conference on document analysis and recognition (ICDAR), pp 256–260

  • Sharma A, Kumar R, Sharma RK (2008) Online handwritten Gurmukhi character recognition using elastic matching. In: Proceedings of the congress on image and signal processing, pp 391–396

  • Sharma DV, Lehal GS, Mehta S (2009) Shape encoded post processing of Gurmukhi OCR. In: Proceedings of the 10th international conference on document analysis and recognition (ICDAR), pp 788–792

  • Sharma DV, Jhajj P (2010) Recognition of isolated handwritten characters in Gurmukhi script. Int J Comput Appl 4(8):9–17

    Google Scholar 

  • Shelke S, Apte A (2011) A multistage handwritten Marathi compound character recognition scheme using neural networks and wavelet features. Int J Signal Process Image Process Pattern Recognit 4(1):81–94

    Google Scholar 

  • Singh A, Bacchuwar K, Bhasin A (2012) A survey of OCR applications. Int J Mach Learn Comput 2(3):314–318

    Article  Google Scholar 

  • Sonkusare M, Sahu N (2016) A survey on handwritten character recognition (HCR) techniques for English alphabets. Adv Vis Comput Int J 3(1):1–12

    Article  Google Scholar 

  • Sopon P, Suksamer T, Polpinij J, Chamchong R (2017) A framework for Thai text retrieval using speech. In: Proceedings of the 6th international conference on computing and informatics, pp 517–522

  • Sreeraj M, Idicula SM (2010) $k$-NN based on-line handwritten character recognition system. In: Proceedings of the 1st international conference on integrated intelligent computing (ICIIC), pp 171–176

  • Srihari SN, Leedham G (2003) A survey of computer methods in forensic handwritten document examination. In: Proceedings of the 11th international graphonomics society conference (IGS), pp 278–281

  • Sundaram S, Ramakrishnan AG (2008) Two dimensional principal component analysis for online character recognition. In: Proceedings of the 11th international conference on frontiers in handwriting recognition (ICFHR), pp 88–94

  • Sundaram S, Ramakrishnan AG (2013) Attention-feedback based robust segmentation of online handwritten isolated Tamil words. ACM Trans Asian Lang Inf Process (TALIP) 12(1):4

    Google Scholar 

  • Sundaram S, Ramakrishnan AG (2014) Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques. Pattern Anal Appl (PAA) 17(3):587–609

    Article  MathSciNet  Google Scholar 

  • Sunija AP, Rajisha RM, Riyas KS (2016) Comparative study of different classifiers for Malayalam dialect recognition system. In: Proceedings of the international conference on emerging trends in engineering, science and technology, pp 1080–1088

  • Swaileh W, Lerouge J, Paquet T (2016) A unified French/English syllabic model for handwriting recognition. In: Proceedings of the 15th international conference on frontiers in handwriting recognition, pp 536–541

  • Tran DC, Franco P, Ogier J (2010) Accented handwritten character recognition using SVM-application to French. In: Proceedings of the 12th international conference on frontiers in handwriting recognition (ICFHR), pp 65–71

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

  • Tsai C (2016) Recognizing handwritten Japanese characters using deep convolutional neural networks, Report, pp 1–7

  • Venkatesh N, Ramakrishnan AG (2011) Choice of classifiers in hierarchical recognition of online handwritten Kannada and Tamil aksharas. J Univers Comput Sci (JUCS) 17:94–106

    Google Scholar 

  • Wang X, Govindaraju V, Srihari S (2000) Holistic recognition of handwritten character pairs. Pattern Recognit 33(12–33):1967–1973

    Article  MATH  Google Scholar 

  • Yap PT, Paramesran R (2003) Image analysis by Krawtcouk moments. IEEE Trans Image Process 12(11):1367–1377

    Article  MathSciNet  Google Scholar 

  • Zhang XZ, Yan CD, Liu XY (1990) Feature point method of Chinese character recognition and its application. J Comput Sci 5(4):305–311

    Google Scholar 

  • Zhu B, Zhou XD, Liu CL, Nakagawa M (2010) A robust model for on-line handwritten Japanese text recognition. Int J Doc Anal Recognit (IJDAR) 13(2):121–131

    Article  Google Scholar 

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Correspondence to Munish Kumar.

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Kumar, M., Jindal, M.K., Sharma, R.K. et al. Character and numeral recognition for non-Indic and Indic scripts: a survey. Artif Intell Rev 52, 2235–2261 (2019). https://doi.org/10.1007/s10462-017-9607-x

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