Abstract
Character recognition is the most challenging research topic due to its diverse applicable environment. Numerous research on Devanagari basic characters has been conducted, but due to difficulties associated, research on handwritten compound characters has received very little attention. The dilemma becomes much more complicated as a result of the different authors writing styles and moods. The traditional machine earning approach of character recognition focuses more on feature extraction, whereas the deep learning approach is a subset of machine learning that uses deep neural networks for learning. For current research work, we have created our own dataset for handwritten Devanagari compound characters. Our dataset has 5000 instances of 50 classes of compound characters collected from various writers of different age groups. This paper presents a convolutional neural network model for the recognition of Devanagari compound characters. We have implemented the ResNet model of CNN and used ReLu as an activation function as it effectively trains deep neural networks. We have implemented three-layer CNN, four-layer CNN, and five-layer CNN on our dataset, and its results are compared. We have achieved the highest accuracy of 100% on our dataset.
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Sachdeva, J., Mittal, S. (2022). Handwritten Offline Devanagari Compound Character Recognition Using CNN. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_18
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DOI: https://doi.org/10.1007/978-981-16-6289-8_18
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