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Improved word-level handwritten Indic script identification by integrating small convolutional neural networks

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Abstract

Handwritten document recognition has been an active domain of research in the field of computer vision for several years since 1914 with the development of handheld scanner for reading printed texts called “optophone”. In India, which has several different scripts in one document page, identifying them is a must to automate process: document understanding. We propose a novel technique in integrating convolutional neural networks (CNNs) for script identification. We combined small individually trainable small CNNs, and used several different levels of variation in the architectures of the individual CNNs. Such a collection of individually trainable modules vary with respect to the input image size, CNN’s depth and wavelet transformation. In our test, we used publicly available dataset of size 11K words (1K per script) from 11 different Indic Scripts: Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu and Urdu. Several ensemble strategies were implemented such as max-voting and probabilistic voting are used in addition to other conventional approaches like feature concatenation. We achieved a maximum accuracy of 95.04%, and it outperforms the accuracy of the state-of-the-art techniques like AlexNet by 2.9% and more importantly, benchmark techniques as (for script identification) on the dataset by more than 4%.

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Acknowledgement

This work is supported by the project order no. SB/S3/EECE/054/2016, dated 25/11/2016, sponsored by SERB (Government of India) and carried out at the Centre for Microprocessor Application for Training Education and Research, CSE Department, Jadavpur University, Kolkata, India.

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Authors and Affiliations

  1. Jadavpur University, Kolkata, WB, 700032, India

    Soumya Ukil, Swarnendu Ghosh & Nibaran Das

  2. Aliah University, Kolkata, WB, 700156, India

    Sk Md Obaidullah

  3. The University of South Dakota, Vermillion, SD, 57069, USA

    K. C. Santosh

  4. West Bengal State University, Kolkata, 700126, WB, India

    Kaushik Roy

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  1. Soumya Ukil
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  2. Swarnendu Ghosh
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  3. Sk Md Obaidullah
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  4. K. C. Santosh
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  5. Kaushik Roy
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  6. Nibaran Das
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Correspondence to Nibaran Das.

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Ukil, S., Ghosh, S., Obaidullah, S.M. et al. Improved word-level handwritten Indic script identification by integrating small convolutional neural networks. Neural Comput & Applic 32, 2829–2844 (2020). https://doi.org/10.1007/s00521-019-04111-1

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