Abstract
In this digital era, it is crucial to identify the authenticity of the words where the writer identification becomes a big challenge. This paper highlights an efficient approach to recognize the character from hand written document using k-nearest neighbor algorithm. Then, a supervised-learning algorithm is employed to recognize the character. From the experimental results, it is observed with our proposed model, we achieved about 92% accuracy for the digits and about 94.15% accuracy for English alphabets. To see the merits of the proposed model, comparison is made against the state-of-the-art models.
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Mishra, A., Kumar, K., Kumar, P., Mittal, P. (2020). A Novel Approach for Handwritten Character Recognition Using K-NN Classifier. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_81
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DOI: https://doi.org/10.1007/978-981-15-0751-9_81
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