Analysis of Performance Measures for the Development of DEVNAGARI Character Recognition System : A Review

Authors

  • Shrutika C. Wankhade  ME Student, Department of Electronics and Tele Communication, HVPM’s College of Engineering & Technology, Amravati, Maharashtra, India
  • Prashant M. Kakde  Assistant Professor, Department of Electronics and Tele Communication, HVPM’s College of Engineering & Technology, Maharashtra, Amravati, India

Keywords:

Online Character Recognition (OCR), Classifiers, Raspberry Pie, Performance Analysis.

Abstract

Devanagari script is widely used in the Indian subcontinent in several major languages such as Hindi, Sanskrit, Marathi and Nepali. Recognition of unconstrained (Handwritten) Devanagari character is more complex due to shape of constituent strokes. Hence character recognition has been an active area of research till now and it continues to be a challenging research topic due to its diverse applicable environment. As the size of the vocabulary increases, the complexity of algorithms also increases linearly due to the need for a larger search space. Devnagari script recognition systems using Zernike moments, fuzzy rule and quadratic classifier provide less accuracy and less efficiency. Classification methods based on learning from examples have been widely applied to character recognition have brought forth significant improvements of recognition accuracies. In this work technique like Artificial Neural Network, Support Vector Machines, Particle Swarm Optimization and Genetics Algorithm are implemented for showing the recognized Devnagari character by using Raspberry Pie.

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International Journal of Scientific Research in Science and Technology

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Published

2017-02-28

Issue

Volume 3, Issue 1, January-February-2017

Section

Research Articles

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How to Cite

[1]
Shrutika C. Wankhade, Prashant M. Kakde, " Analysis of Performance Measures for the Development of DEVNAGARI Character Recognition System : A Review, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 1, pp.385-389 , January-February-2017.