Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Feature Selection Approaches in Online Bangla Handwriting Recognition

  • Conference paper
  • First Online:
Computational Intelligence in Communications and Business Analytics (CICBA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1956))

  • 120 Accesses

Abstract

Any feature selection technique aims to identify a smaller subset of essential characteristics from a larger collection by eliminating those that are redundant, noisy, or irrelevant. Feature selection techniques have proven to be a major ground-breaking technique to save computing time while strengthening prediction accuracy and data interpretation and better cognition of data in machine learning as well as pattern recognition algorithms. In the present experiment, we have compared four well-known feature selection techniques, Harmony Search, Krill Herd Algorithm, Principal Component Analysis and mRMR Algorithm when applied on the feature set produced by combining the frechet distance and distance-based features (256-dimension feature vector). The experiment has been performed on a 10000 online handwritten Bangla character database. The experimental outcome metrics and the analysis of the performances with respect to the percentage of dimensionality reduction and achieved accuracy are presented in a nutshell.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

Similar content being viewed by others

References

  1. Sen, S., Chakraborty, J., Chatterjee, S., Mitra, R., Sarkar, R., Roy, K.: Online handwritten Bangla character recognition using Frechet distance and distance based features. In: Sundaram, S., Harit, G. (eds.) DAR 2018. CCIS, vol. 1020, pp. 65–73. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9361-7_6

    Chapter  Google Scholar 

  2. Zafar, M.F., Mohamad, D., Othman, R.M.: On-line handwritten character recognition: an implementation of counterpropagation neural net. In: Proceedings of World Academy of Science, Engineering and Technology (2005)

    Google Scholar 

  3. Garain, U., Chaudhuri, B.B., Pal, T.T.: Online handwritten Indian script recognition: a human motor function based framework. In: Proceedings of the 16th International Conference on Pattern Recognition, pp. 164–167 (2002)

    Google Scholar 

  4. Connell, S.D., Jain, A.K.: Template-based online character recognition. Pattern Recogn. 34(1), 1–14 (2001)

    Article  MATH  Google Scholar 

  5. Langley, P., Blum, A.L.: Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245–271 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  6. Isabelle, G., Elisseff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  7. Jain, A., Guyon, D.Z.: Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 153–158 (1997)

    Article  Google Scholar 

  8. Zhang, B., Fu, M., Yan, H.: A nonlinear neural network model of mixture of local principal component analysis: application to handwritten digits recognition. Pattern Recogn. 34(2), 203–214 (2001)

    Article  MATH  Google Scholar 

  9. Fischer, A., Bunke, H.: Kernel PCA for HMM-based cursive handwriting recognition. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 181–188. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03767-2_22

    Chapter  Google Scholar 

  10. Singh, P., Verma, A., Chaudhari, N.S.: Devanagri handwritten numeral recognition using feature selection approach. Int. J. Intell. Syst. Appl. 6(12), 40–47 (2014)

    Google Scholar 

  11. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Google Scholar 

  12. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)

    Article  Google Scholar 

  13. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)

    Article  Google Scholar 

  14. Oh, I.S., Lee, J.S., Moon, B.R.: Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1424–1437 (2004)

    Article  Google Scholar 

  15. Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006)

    Article  Google Scholar 

  16. Liwicki, M., Bunke, H.: Feature selection for HMM and BLSTM based handwriting recognition of whiteboard notes. Int. J. Pattern Recognit Artif Intell. 23(5), 907–923 (2009)

    Article  Google Scholar 

  17. Huang, B.Q., Kechadi, M.: A fast feature selection model for online handwriting symbol recognition. In: 5th International Conference on Machine Learning and Applications, pp. 251–257 (2006)

    Google Scholar 

  18. Deepu, V., Madhvanath, S., Ramakrishnan, A.G.: Principal component analysis for online handwritten character recognition. In: Proceedings of 17th International Conference on Pattern Recognition, pp. 327–330 (2004)

    Google Scholar 

  19. Sundaram, S., Ramakrishnan, A.G.: Two dimensional principal component analysis for online tamil character recognition. In: Proceedings of 11th International Conference Frontiers in Handwriting Recognition, pp. 88–94 (2008)

    Google Scholar 

  20. Prasad, G.K., Khan, I., Chanukotimath, N.R., Khan, F.: On-line handwritten character recognition system for Kannada using principal component analysis approach: for handheld devices. In: World Congress on Information and Communication Technologies, pp. 675–678 (2012)

    Google Scholar 

  21. Prasad, G.K., Khan, I., Chanukotimath, N.: On-line Hindi handwritten character recognition for mobile devices. In: Proceedings of International Conference on Advances in Computing, Communications and Informatics, pp. 1074–1078 (2012)

    Google Scholar 

  22. Sen, S., Mitra, M., Bhattacharyya, A., Sarkar, R., Schwenker, F., Roy, K.: Feature selection for recognition of online handwritten Bangla characters. Neural Process. Lett. 50, 2281–2304 (2019)

    Article  Google Scholar 

  23. Ferreira, A.J., Figueiredo, M.A.T.: Efficient feature selection filters for high-dimensional data. Pattern Recognit. Lett. 33(13), 1794–1804 (2012)

    Article  Google Scholar 

  24. Saha, S., et al.: Feature selection for facial emotion recognition using cosine similarity-based harmony search algorithm. Appl. Sci. 10(8), 2816 (2020)

    Article  Google Scholar 

  25. Bhattacharya, U., Gupta, B.K., Parui, S.K.: Direction code based features for recognition of online Handwritten characters of Bangla. In: International Conference on Document Analysis and Recognition, pp. 58–62 (2007)

    Google Scholar 

  26. Bag, S., Bhowmick, P., Harit, G.: Recognition of Bengali handwritten characters using skeletal convexity and dynamic programming. In: International Conference on Emerging Application of Information Technology, pp. 265–268 (2011)

    Google Scholar 

  27. Roy, K.: Stroke-database design for online handwriting recognition in Bangla. Int. J. Mod. Eng. Res. 2, 2534–2540 (2012)

    Google Scholar 

  28. Sen, S., Sarkar, R., Roy, K., Hori, N.: Recognize online handwritten Bangla characters using Hausdorff distance based feature. In: 5th International Conference on Frontiers in Intelligent Computing: Theory and Application, pp. 541–549 (2016)

    Google Scholar 

  29. Sen, S., Bhattacharyya, A., Singh, P.K., Sarkar, R., Roy, K., Doermann, D.: Application of structural and topological features to recognize online handwritten bangla characters. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 17(3), 1–16 (2018)

    Article  Google Scholar 

  30. Sen, S., Shaoo, D., Mitra, M., Sarkar, R., Roy, K.: DFA-based online bangla character recognition. In: Chandra, P., Giri, D., Li, F., Kar, S., Jana, D.K. (eds.) Information Technology and Applied Mathematics. AISC, vol. 699, pp. 175–183. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-7590-2_13

    Chapter  Google Scholar 

  31. Sen, S., Sarkar, R., Roy, K.: An approach to stroke-based online handwritten bangla character recognition. In: Proceedings of the Advanced Computing and Systems for Security, pp. 153–163 (2017)

    Google Scholar 

  32. Ghosh, R.: A novel feature extraction approach for online Bengali and Devanagari character recognition. In: International Conference on Signal Processing and Integrated Networks, pp. 483–488 (2015)

    Google Scholar 

  33. Shin, J., et al.: Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods (2022)

    Google Scholar 

  34. Begum, N., et al.: User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model (2021)

    Google Scholar 

  35. Ruiz-Parrado, V., Heradio, R., Aranda-Escolastico, E., Sánchez, A., Vélez, J.F.: A bibliometric analysis of off-line handwritten document analysis literature (1990–2020). Pattern Recogn. 1(125), 108513 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TISA Lab, Department of Computer Science, West Bengal State University, Kolkata, India

    Bubai Das, Himadri Mukherjee & Kaushik Roy

  2. Department of Computer Science and Engineering (AI-ML), Techno Main Salt Lake, Kolkata, India

    Shibaprasad Sen

Authors
  1. Bubai Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shibaprasad Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Himadri Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaushik Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bubai Das .

Editor information

Editors and Affiliations

  1. Kalyani Government Engineering College, Kalyani, India

    Kousik Dasgupta

  2. Assam University, Silchar, India

    Somnath Mukhopadhyay

  3. University of Kalyani, Kalyani, West Bengal, India

    Jyotsna K. Mandal

  4. Visvabharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Das, B., Sen, S., Mukherjee, H., Roy, K. (2024). Feature Selection Approaches in Online Bangla Handwriting Recognition. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1956. Springer, Cham. https://doi.org/10.1007/978-3-031-48879-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48879-5_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48878-8

  • Online ISBN: 978-3-031-48879-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation