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Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition

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Abstract

This paper presents a deep learning architecture modified for resource-constrained environments, called Non-Fully-Connected Network or NFC-Net, based on convolutional neural network architecture in order to solve the problem of Indic script recognition from handwritten word images. NFC-Net mainly targets resource constraint environment where there is a limited computation power or inadequate training samples or restricted training time. Previous approaches to handwritten script recognition included handcrafted features such as structure-based features and texture-based features. In contrast, here our model learns relatively different features from raw input pixels using NFC-Net. Various parameters of the NFC-Net are adjusted to present a vast and comprehensive study of the neural net in the domain of handwritten script recognition. In order to evaluate the performance of the NFC-Net with suitable parameter estimation, a dataset of 18,000 handwritten multiscript word images consisting of 1500 text words from each of the 12 officially recognized Indic scripts has been considered and a maximum script recognition accuracy of 96.30% is noted. Our proposed model also performs better than some of the recently published script recognition methods in bi-script, tri-script, tetra-script and 12-script scenarios. It has been additionally tested on the RaFD and BHCCD datasets with improved results to prove dataset independency of our model.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Kolkata, West Bengal, 700032, India

    Soumyadeep Kundu, Sayantan Paul, Pawan Kumar Singh, Ram Sarkar & Mita Nasipuri

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  1. Soumyadeep Kundu
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  2. Sayantan Paul
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  3. Pawan Kumar Singh
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  4. Ram Sarkar
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  5. Mita Nasipuri
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Correspondence to Pawan Kumar Singh.

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Kundu, S., Paul, S., Singh, P.K. et al. Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition. Neural Comput & Applic 32, 7879–7895 (2020). https://doi.org/10.1007/s00521-019-04235-4

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  • DOI: https://doi.org/10.1007/s00521-019-04235-4

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