Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Online handwriting character recognition is gaining attention from the researchers across the world because with the advent of touch based devices, a more natural way of communication is being explored. Stroke based online recognition system is proposed in this paper for a very complex Gurmukhi script. In this effort, recognition for 35 basic characters of Gurmukhi script has been implemented on the dataset of 2019 Gurmukhi samples. For this purpose, 32 stroke classes have been considered. Three types of features have been extracted. Hybrid of these features has been proposed in this paper to train the classification models. For stroke classification, three different classifiers namely, KNN, MLP and SVM are used and compared to evaluate the effectiveness of these models. A very promising “stroke recognition rate” of 94% by KNN, 95.04% by MLP and 95.04% by SVM has been obtained.

     

     


  • Keywords


    Feature extraction; Gurmukhi script; K-nearest neighbor; online recognition; Support Vector Machines.

  • References


      [1] K. Verma, R.K. Sharma, “Comparison of HMM and SVM based stroke classifiers for Gurmukhi script” Neural Computing and Applications, vol. 28, no.1, pp. 51-63, 2017.

      [2] N. Gupta, M. Gupta, R. Agrawal, “Preprocessing of Gurmukhi Strokes in Online Handwriting Recognition”, 3rd International Conference on Information Security and Artificial Intelligence (ISAI), vol. 56, pp. 163-168, 2012.

      [3] A. Rekha, “Offline Handwritten Gurmukhi Character and Numeral Recognition using Different Feature Sets and Classifiers - A Survey”, International Journal of Engineering Research and Applications, vol. 2, pp. 187-191, 2012.

      [4] Q.T.A. Safdar, K.U. Khan, “Online Urdu Handwritten Character Recognition: Initial Half Form Single Stroke Characters”, IEEE 12th International Conference on Frontiers of Information Technology, pp. 292-297, 2014.

      [5] S.K. Parui, K. Guin. U. Bhattacharya, B.B. Chaudhuri, “Online Handwritten Bangla character recognition using HMM”, IEEE 19th International Conference on Pattern Recognition (ICPR), pp.1-4, 2008.

      [6] M.K. Mahto, K. Bhatia, R.K. Sharma, “Combined Horizontal and Vertical Projection Feature Extraction Technique for Gurmukhi Handwritten Character Recognition”, IEEE International Conference on Advances in Computer Engineering and Applications (ICACEA), pp. 59-65, 2015.

      [7] A. Sharma, R. Kumar, R.K. Sharma, “Online Handwritten Gurmukhi Character Recognition Using Elastic Matching”, IEEE proceedings of International Congress on Image and Signal Processing, pp. 391-396, 2008.

      [8] G. Singh, M. Sachan, “A Framework of Online Handwritten Gurmukhi Script Recognition”, International Journal of Computer Science and Technology, vol. 6, pp. 52-56, 2015.

      [9] H. Almuallim, S. Yamaguchi, “A Method of Recognition of Arabic Cursive Handwriting”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, pp. 715-722, 1987.

      [10] G.S. Lehal, C. Singh, “A Gurmukhi script recognition system”, IEEE 15th International Conference on Pattern Recognition, vol. 2, pp. 557-560, 2000.

      [11] K. Aparna, V. Subramanian, M. Kasirajan, G.V. Prakash, V. Chakravarthy, S. Madhvanath, “Online handwriting recognition for Tamil”, IEEE Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 438-443, 2004.

      [12] N. Joshi, G. Sita, A. Ramakrishnan, S. Madhvanath, “Comparison of elastic matching algorithms for online Tamil handwritten character recognition”, IEEE Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 444–449, 2004.

      [13] A. Jayaraman, S.C. Chandra, C.V. Srinivasa, “Modular approach to recognition of strokes in Telugu script”, IEEE Ninth International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 501–505, 2007.

      [14] U. Bhattacharya, B.K. Gupta, S.K. Parui, “Direction code based features for recognition of online handwritten characters of Bangla”, IEEE ninth international conference on document analysis and recognition (ICDAR), vol. 1, pp. 58–62, 2007.

      [15] A. Sharma, R. Kumar, R.K. Sharma, “Rearrangement of Recognized Strokes in Online Handwritten Gurmukhi Words Recognition” IEEE 10th International Conference on Document Analysis and Recognition, pp. 1241-1245, 2009.

      [16] T. Mondal, U. Bhattacharya, S.K. Parui, K. Das, “Online handwriting recognition of Indian scripts – the first benchmark”, IEEE 12th International Conference on Frontiers in Handwriting Recognition, pp. 200-205, 2010.

      [17] K.S. Siddharth, M. Jangid, R. Dhir, R. Rani, “Handwritten Gurmukhi Character Recognition using Statistical and Background Directional Distribution Features”, International Journal on Computer Science and Engineering, vol. 3, pp. 2332-2345, 2011.

      [18] D. Wadhwa, K. Verma, “Online Handwriting Recognition of Hindi Numerals using SVM”, International Journal of Computer Application, vol. 48, pp. 13-17, 2012.

      [19] G. Singh, M. Sachan, “Multi-Layer Perceptron (MLP) Neural Network Technique for Offline Handwritten Gurmukhi Character Recognition”, IEEE International Conference on Computational Intelligence and Computing Research, pp. 1-5, 2014.

      [20] S. Sen, A. Bhattacharyya, P.K. Singh, R. Sarkar “Application of Structural and Topological Features to Recognize Online Handwritten Bangla Characters”, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 17 no. 3, 2018.

      [21] K. Sujala, A. James, C. Saravanan, "A hybrid approach for feature extraction in Malayalam handwritten character recognition", Second International Conference on Electrical, Computer and Communication Technologies (ICECCT),pp. 1-8, 2017.


 

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Article ID: 16753
 
DOI: 10.14419/ijet.v7i3.4.16753




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