Abstract
This paper recognizes online handwritten Gurmukhi characters and words. The neural network-based recognition for online handwritten Gurmukhi characters and words has been observed first time in this study. In this work, a scheme is proposed to develop a feature vector and its use as an input to neural network recognition engine. A set of low-level, high-level, and Gabor features are extracted, and a feed-forward neural network is trained to recognize 40 classes of Gurmukhi characters. This work implements rearrangement of strokes stage after recognition and post-processing stages. The results have been achieved as 93.53% and 83.69% for 4511 Gurmukhi characters and 2576 Gurmukhi words, respectively.
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Singh, S., Sharma, A. (2021). Recognition of Online Handwritten Gurmukhi Characters Through Neural Networks. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_18
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DOI: https://doi.org/10.1007/978-981-15-5341-7_18
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