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Emerging Technologies in Data Mining and Information Security pp 111–122Cite as

Bangla Handwritten Digit Recognition Using Convolutional Neural Network

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 755)

Abstract

Handwritten digit recognition has always a big challenge due to its variation of shape, size, and writing style. Accurate handwritten recognition is becoming more thoughtful to the researchers for its educational and economic values. There had several works been already done on the Bangla Handwritten Recognition, but still there is no robust model developed yet. Therefore, this paper states development and implementation of a lightweight CNN model for classifying Bangla Handwriting Digits. The proposed model outperforms any previous implemented method with fewer epochs and faster execution time. This Model was trained and tested with ISI handwritten character database Bhattacharya and Chaudhuri (IEEE Trans Pattern Anal Mach Intell 31:444–457, 2009, [1], BanglaLekha Isolated Biswas et al. (Data Brief 12, 103–107, 2017, [2]) and CAMTERDB 3.1.1 Sarkar et al. (Int J Doc Anal Recogn (IJDAR) 15(1):71–83, 2012, [3]). As a result, it was successfully achieved validation accuracy of 99.74% on ISI handwritten character database, 98.93% on BanglaLekha Isolated, 99.42% on CAMTERDB 3.1.1 dataset and lastly 99.43% on a mixed (combination of BanglaLekha Isolated, CAMTERDB 3.1.1 and ISI handwritten character dataset) dataset. This model achieved the best performance on different datasets and found very lightweight, it can be used on a low processing device like-mobile phone.The pre-train model and code for all these datasets can be found on this link https://github.com/shahariarrabby/Bangla_Digit_Recognition_CNN.

Keywords

  • Bangla handwritten recognition
  • Convolutional neural network
  • Pattern recognition
  • Deep learning
  • Computer vision
  • Machine learning

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Daffodil International University, Dhanmondi, Dhaka, 1205, Bangladesh

    AKM Shahariar Azad Rabby, Sheikh Abujar, Sadeka Haque & Syed Akhter Hossain

Authors
  1. AKM Shahariar Azad Rabby
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  2. Sheikh Abujar
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  3. Sadeka Haque
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  4. Syed Akhter Hossain
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Corresponding author

Correspondence to Sheikh Abujar .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Prof. Dr. Ajith Abraham

  2. Department of Computer and Systems Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Prof. Dr. Paramartha Dutta

  3. Department of Computer Science and Engineering, University of Kalyani, Kalyani, India

    Prof. Jyotsna Kumar Mandal

  4. Institute of Engineering and Management, Kolkata, West Bengal, India

    Abhishek Bhattacharya

  5. Institute of Engineering and Management, Kolkata, West Bengal, India

    Prof. Soumi Dutta

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Rabby, A.S.A., Abujar, S., Haque, S., Hossain, S.A. (2019). Bangla Handwritten Digit Recognition Using Convolutional Neural Network. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_11

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