Abstract
One of the major reasons for poor recognition rate in handwritten character recognition is the lack of unique features to represent handwritten characters. In this paper, an attempt is made to utilize the similarity already exist in different parts of the Gujarati characters. A novel feature extraction technique based on normalized cross correlation is proposed for handwritten Gujarati character recognition. An overall accuracy of 53.12%, 68.53%, and 66.43% is obtained using Naive Bayes classifier, linear and polynomial Support Vector Machine (SVM) classifiers, respectively, with the proposed feature extraction algorithm. Experimental results show significant contribution by proposed technique and improvement in recognition rate may be obtained by combining these features with some other significant features. One of the significant contributions of proposed work is the development of large and representative dataset of 20,500 isolated handwritten Gujarati characters.
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Acknowledgements
The authors are thankful to Institute of Technology, Nirma University for their support to carry out this research.
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Sharma, A.K., Adhyaru, D.M., Zaveri, T.H. (2018). A Novel Cross Correlation-Based Approach for Handwritten Gujarati Character Recognition. In: Somani, A., Srivastava, S., Mundra, A., Rawat, S. (eds) Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-10-5828-8_48
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DOI: https://doi.org/10.1007/978-981-10-5828-8_48
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