An Analytical Study of Handwritten Character Recognition

Ujwal Singh Vohra*, Shriprakash Dwivedi**, Hardwari Lal Mandoria***
* PG Scholar, Department of Information Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, India.
** Assistant Professor, Department of Information Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, India.
*** Professor & Head, Department of Information Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, India.
Periodicity:December - February'2016
DOI : https://doi.org/10.26634/jpr.2.4.5946

Abstract

Handwritten Character Recognition is a crucial part of Optical Character Recognition (OCR) through which the computer understands the handwriting of individuals automatically from the image of a handwritten script. From a decade, OCR becomes the most important application of Pattern Recognition, Machine Vision and Signal Processing for the rapid growth of technology, which can be described as the Electronic or Mechanical conversion of the captured or scanned image. The image is converted into the machine encoded form that can be further used in machine translation, text to speech conversion, text mining and the storage of data. Selections of appropriate feature extraction and classification methods are the crucial factors for achieving a higher rate of recognition with greater level of accuracy for handwritten characters to accurately achieve recognition of each and every letter. Here, in this paper the authors attempt to give a more elaborative image for a comprehensive review that has been proposed to achieve a deep study of the handwritten characters recognition, and this data will be useful for the readers working in the field of handwritten character recognition.

Keywords

OCR, Machine Encoded Form, Translation, Text Mining, Feature Extraction.

How to Cite this Article?

Vohra, U. S., Dwivedi, S. P., and Mandoria, H. L. (2016). An Analytical Study of Handwritten Character Recognition. i-manager’s Journal on Pattern Recognition, 2(4), 26-41. https://doi.org/10.26634/jpr.2.4.5946

References

[1]. A. Kundu, and Y. He, (1991). “On optimal order in modeling sequence of letters in words of common language as a Markov chain”. Pattern Recognit., Vol.24, No.7, pp.603-608.
[2]. Anoop M. Namboodiri, and Anil K. Jain, (2004). “Online Handwritten Script Recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.1.
[3]. Anil K. Jain, Robert P.W. Duin, and Jianchang Mao, (2000). “Statistical Pattern Recognition: A Review”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp.4-37.
[4]. Arjun Singh, and Kansham Angphun Maring, (2015). “Handwritten Devanagari Character Recognition using SVM and ANN”. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 8, pp. 123-128.
[5]. Anshul Gupta, Manisha Srivastava, and Chitralekha Mahanta, (2011). “Offline Handwritten Character Recognition Using Neural Network”. IEEE (ICCAIE), pp. 102- 107.
[6]. B. Hari Kumar, Y.E. Vasanth Kumar and Mohammad Shaffi, (2014). “Optical character recognition using Split Profile Algorithm”. International Journal of Advanced Engineering and Global Technology, Vol.2, No.12.
[7]. Brijmohan Singh, Ankush Mittal, M.A. Ansari and Debashis Ghosh, (2011). “Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach”. IJCSE, Vol.3, No. 4, pp. 1658- 1665.
[8]. Chanchal Bansal, and Arif Khan, (2014). “Handwritten Numeral Recognition using SVM and Chain Code”. IJARET, Vol.2, No.7.
[9]. C.Y. Suen, M. Berthod, and S. Mori, (1980). “Automatic recognition of handprinted characters—The state of the art”. Proc. IEEE, Vol.68, pp.469-487.
[10]. C. V. Jawahar, M. N. S. S. K. Pavan Kumar and S. S. Ravi Kiran, (2003). “A Bilingual OCR for Hindi-Telugu Documents and its Applications”. Seventh International Conference on Document Analysis and Recognition (ICDAR).
[11]. Deepa Berchmans, and S.S. Kumar, (2014). “Optical Character Recognition: An Overview and an Insight”. IEEE, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 1361-1365.
[12]. Divakar Yadav, Sonia Sánchez-Cuadrado and Jorge Morato, (2013). “Optical Character Recognition for Hindi Language Using a Neural-network Approach”. Journal of Information Processing Systems, Vol.9, No.1, pp.117-140.
[13]. Gaurav Y. Tawde, (2014). “Optical Character Recognition for Isolated Offline Handwritten Devanagari Numerals Using Wavelets”. Journal of Engineering Research and Applications, Vol.4, No.2.
[14]. H. Cheng, W. H. Hsu, and M. C. Kuo, (1993). “Recognition of handprinted Chinese characters via stroke relaxation”. Pattern Recognit., Vol.26, No.4, pp.579-593.
[15]. I.S. Oh and C. Y. Suen, (1998). “Distance features for neural network-based recognition of handwritten characters”. Int. J. Document Anal.Recognit., Vol.1, No.2, pp.73-88.
[16]. J. Rocha and T. Pavlidis, (1994). “A shape analysis model”. IEEE Trans. Pattern Anal. Machine Intell., Vol.16, pp.394-404.
[17]. J. Kitler, (1986). “Feature selection and extraction”. In Handbook of Pattern Recognition and Image Processing. T. Young and K. Fu, Eds, NewYork: Academic, pp.59-83.
[18]. J. Hu, and T. Pavlidis, (1996). “A Hierarchical Approach to Efficient Curvilinear Object Searching”. Computer Vision and Image Understanding, Vol.63, No.2, pp.208-220.
[19]. K. Y. Rajput and Sangeeta Mishra, “Recognition and Editing of Devnagari Handwriting Using Neural Network”. SPIT-IEEE Colloquium and Intl. Conference, Mumbai, India.
[20]. K. Chung and J. Yoon, (1997). “Performance comparison of several feature selection methods based on node pruning in handwritten character recognition”. In Proc. 4 Int. Conf. Document Anal. Recognit., Ulm, Germany, pp.11-15.
[21]. K. Wang, Y.Y. Tang, and C.Y. Suen, (1988). “Multilayer projections for the classification of similar Chinese characters”. In Proc. 9 Int. Conf. Pattern Recognit., pp.842-844.
[22]. K.T. Miura, R. Sato, and S. Mori, (1997). “A method of extracting curvature features and its application to handwritten character recognition”. In Proc. 4 Int. Conf. Document Anal. Recognit., Ulm, Germany, pp.450-454.
[23]. M.D. Garris, et al., (1994). “NIST form based handprinted recognition system”. Nat. Inst. Standard Technol., Gaithersburg, MD, Tech. Rep. NISTIR 546 920 899.
[24]. M. Egmont-Petersen, and D. De Ridder, H. Handels, (2002). “Image Processing with Neural Networks: A Review”. Pattern Recognition, Vol.35, pp.2279-2301.
[25]. M. Hamanaka, K. Yamada, and J. Tsukumo, (1993). “On-Line Japanese Character Recognition Experiments by an Off-Line Method Based on Normalization cooperated Feature Extraction”. In Proc. 3 ICDAR, pp.204- 207.
[26]. M.K. Brown and S. Ganapathy, (1983). “Preprocessing techniques for cursive script word recognition”. Pattern Recognit., Vol.16, No.5, pp.447-458.
[27]. Munish Kumar, M.K. Jindal and R. K. Sharma, (2011). “k -Nearest Neighbor Based Offline Handwritten Gurmukhi Character Recognition”. IEEE ICIIP.
[28]. Mohamed Cheriet, Nawwaf Kharma, and Cheng- Lin Liu, Ching Y. Suen, (2007). Character Recognition Systems. A Guide for Students and Practioners. John Wiley & Sons, Inc.
[29]. Malakar, Samir, et al., (2012). “Text line extraction from handwritten document pages using spiral run length smearing algorithm”. Communications, Devices and Intelligent Systems (CODIS), International Conference on, IEEE.
[30]. M N S S K Pavan Kumar, S S Ravikiran, Abhishek Nayani, C V Jawahar and P J Narayanan, (2003). “Tools for Developing OCRs for Indian Scripts”.Computer Vision and Pattern Recognition Workshop, 2003, CVPRW’03, Conf. on, IEEE, Vol. 3, pp. 33.
[31]. Monica Patel, and Shital P. Thakkar, (2015). “Handwritten Character Recognition in English: A Survey”. International Journal of Advanced Research in Computer and Communication Engineering, Vol.4, No.2.
[32]. Mohammad Abu Obaida, Md. Jakir Hossain, Momotaz Begum and Md. Shahin Alam, (2011). “Multilingual OCR (MOCR): An Approach to Classify Words to Languages”. International Journal of Computer Applications, Vol.32, No.1.
[33]. Nabin. Sharma, U. Pal, F. Kimura, and S. Pal, (2006). “Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier”. Springer-Verlag, Berlin Heidelberg ICVGIP, LNCS 4338, pp.805-816.
[34]. Nafiz Arica, and Fatos T. Yarman-Vural, (2001). “An Overview of Character Recognition Focused on Off-Line Handwriting”. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol.31, No.2.
[35]. N. Otsu, (1979). “A threshold selection method from gray-level histograms”. IEEE Transactions on Systems, Man, and Cybernetics SMC, Vol.9(1), pp.62-66.
[36]. N. Arica and F.T. Yarman-Vural, (2000). “One dimensional representation of two dimensional information for HMM based handwritten recognition”. Pattern Recognit. Lett., Vol.21, No.6-7, pp.583-592.
[37]. Nafiz Arica, (1998). “An Offline Character Recognition System for Free Style Handwriting”. (Doctorial Dissertation), The Middle East Technical University.
[38]. Ohhira T., Pecharanin N., Taguchi A., Iijima, N., Akima, Y., and Sone, M., (1995). “Chinese character recognition by the auto recognition system”. IEEE International Conference on Neural Networks, Vol.5, pp.2222-2225.
[39]. Paul W. Handel, (1993). Statistical Machine. U.S. Patent 1915993, Retrieved From http://www.google.com/ patents? vid=USPAT1915993.
[40]. P.S. Deshpande Latesh Malik, and Sandhya Arora, (2007). “Recognition of Handwritten Devanagari Characters with Percentage Component Regular Expression Matching and Classification Tree”. TENCON 2007-2007, IEEE Region 10 Conference, pp.1-4.
[41]. P.D. Gader and M. A. Khabou, (1996). “Automatic feature generation for handwritten digit recognition”. IEEE Trans. Pattern Anal. Machine Intell., Vol.18, pp.1256-1262.
[42]. R.J. Ramteke, and S.C. Mehrotra, (2008). “Recognition of Handwritten Devnagari Numerals”. International Journal of Computer Processing of Oriental Languages.
[43]. Rekha Singh, (2014). “Character Recognition for Bi- Lingual Mixed-type Characters using Artificial Neural Network”. IJRET: International Journal of Research in Engineering and Technology, Vol.3, Special Issue.10.
[44]. S.S. Wang, P.C. Chen, and W.G. Lin, (1994). “Invariant pattern recognition by moment Fourier descriptor”. Pattern Recognit., Vol.27, pp.1735-1742.
[45]. S. Madhvanath and V. Govindaraju, (1999). “Reference lines for holistic recognition of handwritten words”. Pattern Recognit., Vol.32, No.12, pp.2021-2028.
[46]. S. Madhvanath, G. Kim, and V. Govindaraju, (1999). “Chaincode contour processing for handwritten word recognition”. IEEE Pattern. Anal. Machine Intell., Vol.21, pp.928-932.
[47]. S. Madhvanath, E. Kleinberg, V. Govindaraju, and S. N. Srihari, (1997). “The HOVER system for rapid holistic verification of off-line handwritten phrases”. In Proc. 4 Int. Conf. Document Anal. Recognit., Ulm, Germany, pp.855- 890.
[48]. S.W. Lee and Y.J. Kim, (1995). “Direct extraction of topographic features for gray-scale character recognition”. IEEE Trans. Pattern Anal. Machine Intell., Vol.17, pp.724-729.
[49]. Sandhya Arora, et al., (2010). “Performance Comparison of SVM and ANN for Handwritten Devanagari Character Recognition”. IJCSI International Journal of Computer Science Issues, Vol.7, No.3.
[50]. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, D. K. Basu and M. Kundu, (n.d.). “ Recognition of Non-Compound Handwritten Devanagari Characters using a Combination of MLP and Minimum Edit Distance”. International Journal of Computer Science and Security (IJCSS), Vol.4, No.1, pp.107-120.
[51]. Shailedra Kumar Shrivastava, and Sanjay S. Gharde, (2010). “Support Vector Machine for Handwritten Devanagari Numeral Recognition”. International Journal of Computer Applications, Vol.7, No.11.
[52]. Sung-Bae Cho, (2002). Fusion of Neural Networks with Fuzzy Logic and Genetic Algorithm. IOS Press, pp.363-372.
[53]. Sanjay Chandra Arya, Rajesh Shyam Singh and Hardwari Lal Mandoria, (2015). “Image Denoising in Hand Written Document for Degraded Documents using Wiener Filter Algorithm”. International Journal for Research in Emerging Science and Technology, Vol.2, No.7.
[54]. Satish Kumar, (2009). “Per formance and Comparison of Features on Devanagari Hand-printed Dataset,” Inlt. Journal of Recent Trends in Engineering, Vol.1, No.2.
[55]. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, M. Kundu and D. K. Basu and L. Malik, (2010). “Handwritten Devanagari Numeral Recognition using SVM & ANN”. IJCSE40, Vol.1, No.2.
[56]. I.K. Sethi and B. Chatterjee, (1976). “Machine Recognition of hand printed Devanagri numerals”. J. Inst. Electron Telecommun, Engg, pp. 532-535.
[57]. Saurabh Farkya, Govinda Surampudi, and Ashwin Kothari, (2015). “Hindi Speech Synthesis by concatenation of recognized Hand written Devanagri Script using Support Vector Machines Classifier”. IEEE ICCSP Conference.
[58]. R.M.K. Sinha, and H.N. Mahabala, (1979). “Machine Recognition of Devanagri Script. IEEE Trans. System. Man Cybern, pp. 435-441.
[59]. Tapan K Bhowmik, Swapan K Parui Utpal Roy, (2008). “Discriminative HMM Training with GA for Handwritten Word Recognition”. IEEE.
[60]. Teena Mittal, and Rajendra Kumar Sharma, (2015). “Multiclass SVM based Spoken Hindi Numerals Recognition”. International Arab Journal of Information Technology, Vol.12, No.6A.
[61]. Umapada Pal, Sukalpa Chanda Tetsushi, Wakabayashi, and Fumitaka Kimura, (n.d.). “Accuracy Improvement of Devnagari Character Recognition Combining SVM and MQDF”.
[62]. U. Pal, T. Wakabayashi, and F. Kimura, (2009). “Comparative Study of Devnagari Handwritten Character Recognition using Different Feature and Classifiers”. 10 Intl. Conf. on Document Analysis and Recognition, pp.1111-1115.
[63]. Vikas J Dongre, and Vijay H Mankar, (2010). “A Review of Research on Devnagari Character Recognition”. International Journal of Computer Applications, Vol.12, No.2, pp.0975- 8887.
[64]. Vijay Laxmi Sahu, and Babita Kubde, (2013). “Offline Handwritten Character Recognition Techniques using Neural Network: A Review”. International Journal of Science and Research (IJSR), Vol.2, Issue.1.
[65]. W. Lu, Y. Ren, and C. Y. Suen, (1991). “Hierarchical attributed graph representation and recognition of handwritten chinese characters”. Pattern Recognit., Vol.24, No.7, pp.617-632.
[66]. W.Y. Kim and P. Yuan, (1994). “A practical pattern recognition system for translation, scale and rotation invariance”. In Proc. Int. Conf. Comput. Vis.Pattern Recognit., Seattle, WA.
[67]. X. Li, W. Oh, J. Hong, and W. Gao, (1997). “Recognizing components of handwritten characters by attributed relational graphs with stable features”. In Proc. 4th Int. Conf. Document Anal. Recognit., pp.616-620.
[68]. X. Zhu, Y. Shi, and S. Wang, (1999). “A new algorithm of connected character image based on Fourier transform”. In Proc. 5th Int. Conf. DocumentAnal. Recognit., Bangalore, India, pp.788-791.
[69]. Y. Tao and Y. Y. Tang, (1999). “The feature extraction of Chinese character based on contour information”. In Proc. 5th Int. Conf. Document Anal.Recognit., Bangalore, India, pp.637-640.
[70]. Y. C. Chim, A. A. Kassim, and Y. Ibrahim, (1999). “Character recognition using statistical moments”. Image Vis. Comput., Vol.17, pp.299-307.
[71]. D. Trier, A.K. Jain, and T. Taxt, (1996). “Feature extraction method for character recognition—A survey”. Pattern Recognit., Vol.29, No.4, pp.641-662.
[72]. (1995). “Multiresolutional recognition of handwritten numerals with wavelet transform and multilayer cluster rd neural network”. In Proc. 3rd Int. Conf. Document Anal. Recognit., Montreal, QC, Canada, pp.1010-1014.
[73]. D. Guillevic and C.Y. Suen,(1998). “Recognition of legal amounts on bank cheques”. Pattern Anal. Applicat., Vol.1, No.1, pp.28- 41.
[74]. (1998). “HMM–KNN word recognition engine for bank cheque processing”. In Proc. 14th Int. Conf. Pattern Recognit., Brisbane, Australia, pp.1526-1529.
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