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Bangla-Meitei Mayek scripts handwritten character recognition using Convolutional Neural Network

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Abstract

Recognition of handwritten characters in two Indic scripts Bangla and Meitei Mayek is one of the challenging responsibilities due to intricate patterns and scarcity of standard datasets. Convolutional Neural Network (CNN) is one of the stablest well-known techniques for classifying objects in distinctive specialties as it has an extraordinary capability of discovering complex patterns. In this paper, we hook a different layout and obtain a unique CNN architecture from scratch, which has manifold advantages over classical machine learning (ML) approaches, and it has a unique ability to consolidate feature extraction and classification altogether. Further, we stretch our work to uncover the mathematical rationale for using non-linearity in the deep learning (DL) model. Our proposed CNN architecture consists of four layers, including convolutional layer (CL), nonlinear activation layer (AL), pooling layer (PL), and fully connected layer (FCL), which are used in the existing two accessible Bangla datasets named cMATERdb and ISI Bangla datasets. The identical model also validates on proposed Manipuri Character dataset, called “Mayek27”. Moreover, we perform an in-depth comparison with different batch sizes and optimization techniques over all the datasets for understanding their functionality. We conceive a novel benchmark performance that has delivered state-of-the-art decisions on two regional handwritten character identifications.

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Notes

  1. https://code.google.com/archive/p/cmaterdb/

  2. https://www.isical.ac.in/~ujjwal/

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Acknowledgements

We appreciate the time and efforts made by the editor and reviewers while reviewing this manuscript. Further, the authors would like to thank the CMATERdb group (Jadavpur University), Prof. Ujjwal Bhattacharya (ISI Kolkata), and Prof. R. Balasubramanian (IIT Roorkee), for their continuous suggestion to improve this paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Abhishek Hazra

  2. Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, 177005, India

    Prakash Choudhary

  3. Department of Computer Science and Engineering, National Institute of Technology, Manipur, 795004, India

    Sanasam Inunganbi

  4. Mobile & Cloud Lab, Institute of Computer Science, Faculty of Science and Technology, University of Tartu, Tartu, 50090, Estonia

    Mainak Adhikari

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  1. Abhishek Hazra
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Correspondence to Abhishek Hazra.

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Hazra, A., Choudhary, P., Inunganbi, S. et al. Bangla-Meitei Mayek scripts handwritten character recognition using Convolutional Neural Network. Appl Intell 51, 2291–2311 (2021). https://doi.org/10.1007/s10489-020-01901-2

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