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Meetei Mayek Natural Scene Character Recognition Using CNN

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Soft Computing and Its Engineering Applications (icSoftComp 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1788))

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Abstract

Recognition of characters present in natural scene images is a nascent and challenging area of research in computer vision and pattern recognition. This paper proposes a convolutional neural network (CNN) based natural scene character recognition system for Meetei Mayek. Meetei Mayek text present in natural scene images have been detected using maximally stable extremal regions (MSER), geometrical properties, strokewidth and distance. The extracted and manually cropped characters have been used to create a small database. The experiments of the proposed CNN have been conducted on the isolated characters of the Meetei Mayek natural scene character database. The proposed system has been compared with different combinations of feature descriptors, extracted using pretrained CNNs - Alexnet, VGG16, VGG19 and Resnet18 employing three classifiers - support vector machine (SVM), multilayer perceptron (MLP) and k-nearest neighbour (K-NN). The proposed system has achieved better performance with a classification accuracy of 97.57%.

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Authors and Affiliations

  1. National Institute of Technology Manipur, Imphal, 795004, India

    Chingakham Neeta Devi

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  1. Chingakham Neeta Devi
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Correspondence to Chingakham Neeta Devi .

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Editors and Affiliations

  1. Charotar University of Science and Technology, Changa, Anand, Gujarat, India

    Kanubhai K. Patel

  2. University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  3. Charotar University of Science and Technology, Changa, Anand, India

    Atul Patel

  4. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

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Devi, C.N. (2023). Meetei Mayek Natural Scene Character Recognition Using CNN. In: Patel, K.K., Santosh, K.C., Patel, A., Ghosh, A. (eds) Soft Computing and Its Engineering Applications. icSoftComp 2022. Communications in Computer and Information Science, vol 1788. Springer, Cham. https://doi.org/10.1007/978-3-031-27609-5_33

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  • DOI: https://doi.org/10.1007/978-3-031-27609-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27608-8

  • Online ISBN: 978-3-031-27609-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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